Assessment and Development of Remote Sensing Based Algorithms for Water Quality Monitoring in Olushandja Dam, North-Central Namibia

Kapalanga, Taimi Sofia (2015) Assessment and Development of Remote Sensing Based Algorithms for Water Quality Monitoring in Olushandja Dam, North-Central Namibia. Masters thesis, University of Zimbabwe.

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Abstract

Olushandja Dam is amongst Namibia’s inland water bodies that store and supply water to towns such as Outapi, Oshikuku and Oshakati. The dam is part of a complex water supply system that transports inter-basin water from the Kunene River Basin into Cuvelai Basin in the north-central regions of Namibia via a canal. There are potential sources of pollution along the route of the canal and around the dam which have effects on the water quality in the canal and eventually in the Olushandja Dam. Therefore, frequent and continuous monitoring of water quality is needed to allow timely decisions on the management of this critical resource. Specifically, the study sought to measure water quality at selected points in the dam and on the canal. This study used Landsat 8, 30 m resolution imagery to derive water quality parameters using retrieval algorithms. Water quality parameters included total suspended matter, turbidity, total nitrogen, nitrates, ammonia, total phosphorus and total algae counts. The study was carried out from November 2014 to June 2015. The retrieval algorithms were developed from a simple regression analysis between reflectance values of satellite images and field measurements. Statistical analyses were carried out to assess correlation between Landsat 8 predicted and field measured data. The field measurements showed that the dam and canal water is of low risk to human and is suitable for livestock watering. Turbidity levels exceeded the recommended limits set by NamWater is thus likely to cause complications in drinking water treatment as well as human and aquatic life. The study also found that all water quality parameter regression algorithms had high correlation coefficients (R2) which was between 0.980-0.999. Therefore, the study concludes that the developed regression algorithms are best fit to predict water quality parameters from satellite data. Remote sensing is therefore recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide information about surface water quality and Namibia has cloud free sky most times of the year. However, accurate monitoring data acquired using traditional methods remain an important input into remote sensing process for prediction of water quality.

Item Type:	Thesis (Masters)
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Africana
Depositing User:	Tim Khabala
Date Deposited:	15 May 2018 08:07
Last Modified:	15 May 2018 08:07
URI:	http://thesisbank.jhia.ac.ke/id/eprint/4008

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