Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2021-08-01
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| Series: | Water Supply |
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| Online Access: | http://ws.iwaponline.com/content/21/5/1878 |
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| author | Taimi S. Kapalanga Zvikomborero Hoko Webster Gumindoga Loyd Chikwiramakomo |
| author_facet | Taimi S. Kapalanga Zvikomborero Hoko Webster Gumindoga Loyd Chikwiramakomo |
| author_sort | Taimi S. Kapalanga |
| collection | DOAJ |
| description | Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally the best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality. HIGHLIGHTS
Over past years, frequent and continuous water quality monitoring has been problematic in Namibia.;
A linear regression can now be used to develop algorithms for retrieving water quality data.;
Good prediction accuracy for turbidity, TN, TP and total algae count.;
More sampling points needed to further improve regression model accuracy.;
Remote sensing provides rapid information on water quality spatio-temporal variability.; |
| first_indexed | 2024-12-17T10:13:20Z |
| format | Article |
| id | doaj.art-f4d3e3d168384441bba9da327b7f9b5f |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-12-17T10:13:20Z |
| publishDate | 2021-08-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-f4d3e3d168384441bba9da327b7f9b5f2022-12-21T21:52:59ZengIWA PublishingWater Supply1606-97491607-07982021-08-012151878189410.2166/ws.2020.290290Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central NamibiaTaimi S. Kapalanga0Zvikomborero Hoko1Webster Gumindoga2Loyd Chikwiramakomo3 Department of Civil Engineering, University of Zimbabwe, Box MP 167 Mt Pleasant, Harare, Zimbabwe Department of Civil Engineering, University of Zimbabwe, Box MP 167 Mt Pleasant, Harare, Zimbabwe Department of Civil Engineering, University of Zimbabwe, Box MP 167 Mt Pleasant, Harare, Zimbabwe Department of Civil Engineering, University of Zimbabwe, Box MP 167 Mt Pleasant, Harare, Zimbabwe Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally the best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality. HIGHLIGHTS Over past years, frequent and continuous water quality monitoring has been problematic in Namibia.; A linear regression can now be used to develop algorithms for retrieving water quality data.; Good prediction accuracy for turbidity, TN, TP and total algae count.; More sampling points needed to further improve regression model accuracy.; Remote sensing provides rapid information on water quality spatio-temporal variability.;http://ws.iwaponline.com/content/21/5/1878landsat 8reflectance valuesregression algorithmsremote sensingretrievalwater quality |
| spellingShingle | Taimi S. Kapalanga Zvikomborero Hoko Webster Gumindoga Loyd Chikwiramakomo Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia Water Supply landsat 8 reflectance values regression algorithms remote sensing retrieval water quality |
| title | Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia |
| title_full | Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia |
| title_fullStr | Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia |
| title_full_unstemmed | Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia |
| title_short | Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia |
| title_sort | remote sensing based algorithms for water quality monitoring in olushandja dam north central namibia |
| topic | landsat 8 reflectance values regression algorithms remote sensing retrieval water quality |
| url | http://ws.iwaponline.com/content/21/5/1878 |
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