Land–Lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA

Mohammad Hajigholizadeh, Angelica Moncada, Samuel Kent and Assefa M. Melesse
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Mohammad Hajigholizadeh: Department of Civil & Environmental Engineering, Florida International University, 10555 W Flagler Street, EC3781, Miami, FL 33174, USA
Angelica Moncada: Department of Earth and Environment, Institute of Environment, Florida International University, AHC-5-390, 11200 SW 8th Street, Miami, FL 33199, USA
Samuel Kent: Department of Earth and Environment, Institute of Environment, Florida International University, AHC-5-390, 11200 SW 8th Street, Miami, FL 33199, USA
Assefa M. Melesse: Department of Earth and Environment, Institute of Environment, Florida International University, AHC-5-390, 11200 SW 8th Street, Miami, FL 33199, USA

Land, 2021, vol. 10, issue 2, 1-17

Abstract: The state of water quality of lakes is highly related to watershed processes which will be responsible for the delivery of sediment, nutrients, and other pollutants to receiving water bodies. The spatiotemporal variability of water quality parameters along with the seasonal changes were studied for Lake Okeechobee, South Florida. The dynamics of selected four water quality parameters: total phosphate (TP), total Kjeldahl nitrogen (TKN), total suspended solid (TSS), and chlorophyll-a (chl-a) were analyzed using data from satellites and water quality monitoring stations. Statistical approaches were used to establish correlation between reflectance and observed water quality records. Landsat Thematic Mapper (TM) data (2000 and 2007) and Landsat Operational Land Imager (OLI) in 2015 in dry and wet seasons were used in the analysis of water quality variability in Lake Okeechobee. Water quality parameters were collected from twenty-six (26) monitoring stations for model development and validation. In the regression model developed, individual bands, band ratios and various combination of bands were used to establish correlation, and hence generate the models. A stepwise multiple linear regression (MLR) approach was employed and the results showed that for the dry season, higher coefficient of determination (R 2 ) were found (R 2 = 0.84 for chl-a and R 2 = 0.67 for TSS) between observed water quality data and the reflectance data from the remotely-sensed data. For the wet season, the R 2 values were moderate (R 2 = 0.48 for chl-a and R 2 = 0.60 for TSS). It was also found that strong correlation was found for TP and TKN with chl-a, TSS, and selected band ratios. Total phosphate and TKN were estimated using best-fit multiple linear regression models as a function of reflectance data from Landsat TM and OLI, and ground data. This analysis showed a high coefficient of determination in dry season (R 2 = 0.92 for TP and R 2 = 0.94 for TKN) and in wet season (R 2 = 0.89 for TP and R 2 = 0.93 for TKN). Based on the findings, the Multiple linear regression (MLR) model can be a useful tool for monitoring large lakes like Lake Okeechobee and also predict the spatiotemporal variability of both optically active (Chl-a and TSS) and inactive water (nutrients) quality parameters.

Keywords: Lake Okeechobee; Landsat; multiple linear regression; chlorophyll-a; total suspended solids; nutrients (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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