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Citation Information

Type Thesis or Dissertation - Master of Science
Title Assessment and development of remote sensing based algorithms for water quality monitoring in Olushandja Dam, North-Central Namibia.
Publication (Day/Month/Year) 2015
URL http://ir.uz.ac.zw/bitstream/handle/10646/3416/Kapalanga_Remote_sensing_based_algorithms_for_water_q​uality_monitoring.pdf?sequence=3&isAllowed=y
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.

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