Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data
The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substit...
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/12/2256 |
| _version_ | 1827690148533370880 |
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| author | Md Mamun Jannatul Ferdous Kwang-Guk An |
| author_facet | Md Mamun Jannatul Ferdous Kwang-Guk An |
| author_sort | Md Mamun |
| collection | DOAJ |
| description | The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (<i>r</i>) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (<i>r</i> = −0.79) and CHL-a (<i>r</i> = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (<i>r</i> = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (<i>r</i> = −0.76). Considering the <i>r</i> values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir. |
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| language | English |
| last_indexed | 2024-03-10T10:33:19Z |
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| spelling | doaj.art-8773ea8c4e3f4a85a7ce7408e37d7dd22023-11-21T23:27:33ZengMDPI AGRemote Sensing2072-42922021-06-011312225610.3390/rs13122256Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM DataMd Mamun0Jannatul Ferdous1Kwang-Guk An2Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, KoreaClimate Change Lab, Department of Civil Engineering, Military Institute of Science and Technology, Mirpur, Dhaka 1216, BangladeshDepartment of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, KoreaThe main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (<i>r</i>) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (<i>r</i> = −0.79) and CHL-a (<i>r</i> = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (<i>r</i> = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (<i>r</i> = −0.76). Considering the <i>r</i> values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir.https://www.mdpi.com/2072-4292/13/12/2256empirical modelsmultiple regressionPaldang Reservoirwater quality parameters |
| spellingShingle | Md Mamun Jannatul Ferdous Kwang-Guk An Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data Remote Sensing empirical models multiple regression Paldang Reservoir water quality parameters |
| title | Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data |
| title_full | Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data |
| title_fullStr | Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data |
| title_full_unstemmed | Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data |
| title_short | Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data |
| title_sort | empirical estimation of nutrient organic matter and algal chlorophyll in a drinking water reservoir using landsat 5 tm data |
| topic | empirical models multiple regression Paldang Reservoir water quality parameters |
| url | https://www.mdpi.com/2072-4292/13/12/2256 |
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