Tanjeck, Akum Ernest2019-09-092019-09-092019https://hdl.handle.net/10539/28063A research report submitted to the Faculty of Science, University of the Witwatersrand in partial fulfillment of the requirements of the degree of Masters of Science (Geographical Information Science & Remote Sensing) Johannesburg, 2019This project aimed to test the use of two multispectral sensors in estimating and mapping chlorophyll-a and turbidity concentrations, at the Vaal Dam, South Africa. Landsat 8 and Sentinel-2 satellite data were acquired on the 04/10/2016 and 13/11/2016 respectively. Image processing was carried out using atmospheric correction by applying the FLAASH model on the calibrated radiance to obtain atmospherically corrected Landsat 8 images. With Sentinel-2 data, the atmospheric correction was performed using Sen2cor from the Sentinel toolbox to obtain a geometrically corrected Sentinel-2 multispectral image. Band reflectance values were extracted from the two remotely sensed data, and laboratory measurements for chlorophyll-a and turbidity concentrations were obtained from samples collected from 23 sampling points at the dam on the 26-28 October 2016. The remotely sensed data were then cross-validated with the field data in mapping and predicting chlorophyll-a and turbidity concentrations. Two regression models were used in this study; multiple stepwise linear regression, and random forest regression models, to predict chlorophyll-a and turbidity concentrations from the remotely sensed data. The performance and accuracy of these regression models were evaluated by correlating the predicted against the observed chlorophyll-a and turbidity concentrations from the satellite data. Random forest regression gave a higher performance and accuracy than stepwise multiple regression based on their R squared and RMSE values. In mapping chlorophyll-a and turbidity concentrations from both remotely sensed datasets, stepwise linear regression analysis was used to derive estimates for chlorophyll-a and turbidity concentrations. The high estimate values were multiplied with their corresponding bands and added up with intercepts (the expected mean values for chlorophyll-a and turbidity) from coefficients derived from the regression analysis. An equation was then developed using the raster calculator in ArcGIS to map chlorophyll-a and turbidity concentrations from the remotely sensed data for the entire dam. The test of the new generation multispectral sensors Landsat 8 and Sentinel-2 using random forest and multiple stepwise regression models in mapping and predicting chlorophyll-a and turbidity concentrations at the dam was a success. The random forest model gave a better performance than multi stepwise regression. In some cases, the performance of the models was poor as a result of the field data collection date not coinciding with the date of satellite data collectionenThesis