Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia

Abstract

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.;

Keywords

• landsat 8
• reflectance values
• regression algorithms
• remote sensing
• retrieval
• water quality