Dalu. TatendaDondofema, F.Mokgoebo, M. J.Muthivhi, Fulufhelo Faith2022-09-202022-09-202022-07-15Muthivhi, F. F. (2021) Assessing the spatiotemporal variation of phytoplankton biomass in Nandoni reservoir in the Vhembe District (South Africa) using LANDSAT satellite imagery. University of Venda. South Africa.<http://hdl.handle.net/11602/2295>.http://hdl.handle.net/11602/2295MENVSCDepartment of Geography and Environmental SciencesChlorophyll–a (chl–a) is an optical active compound used as proxy for phytoplankton biomass to determine the trophic state of the aquatic ecosystem. Traditional approaches for monitoring aquatic system are time consuming, expensive, and non–continuous, therefore, Remote Sensing technologies are qualitative for monitoring the status for water quality in large scale and low cost. The aim of this study was to assess the spatial and temporal variation of phytoplankton biomass in Nandoni reservoir, Limpopo to examine the relationship that exist between the physico–chemical variables and chl–a concentration using readily available Landsat multispectral images. Multispectral resolution of (30 m) Landsat 7 ETM+ and Landsat 8 OLI images for June to December 2008 to 2020 were used to derive the distribution of chl–a concentration. The spatial distribution of chl–a concentration in wet and dry season of these years was obtained. By using regression techniques, in situ measured chl–a was related to construct and validate Landsat predicted chl–a to determine the distribution of chl–a in the reservoir. The results indicate that Landsat derived chl–a was similar with the observed measured chlorophyll–a (R2 = 0.91). There was a negative significant correlation among Land Use and Land Cover with water quality (P > 0.05). Using permutational multivariate analysis of variance (PERMANOVA) analysis, there was significant differences for chl–a concentration in sites, seasons, and zones. There was positive significant correlation observed on water temperature with strong negative significant with salinity and TDS. A strong perfect linear association among predicted vs measured chl–a were found. Chlorophyll–a concentration in Nandoni reservoir was derived using Landsat remote sensing images, suggesting that Landsat sensor is suitable for monitoring small reservoir in a short timescale. Remote sensing techniques can be used to control the development of an early warning system of this study and other reservoirs.1 online resource (viii, 79 leaves) : color illustrations, color mapsenUniversity of VendaUCTDPhytoplankton -- South Africa -- Limpopo579.81770968257Phytoplankton -- South Africa -- LimpopoPlankton -- South Africa -- LimpopoPlants -- South Africa -- LimpopoAlgae -- South Africa -- LimpopoDiatoms -- South Africa -- LimpopoFreshwater productivity (Biology) -- South Africa -- LimpopoWater quality biological assessment -- South Africa -- LimpopoEcological risk assessment -- South Africa -- LimpopoDissertationMuthivhi FF. Assessing the spatiotemporal variation of phytoplankton biomass in Nandoni reservoir in the Vhembe District (South Africa) using LANDSAT satellite imagery. []. , 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2295Muthivhi, F. F. (2022). <i>Assessing the spatiotemporal variation of phytoplankton biomass in Nandoni reservoir in the Vhembe District (South Africa) using LANDSAT satellite imagery</i>. (). . Retrieved from http://hdl.handle.net/11602/2295Muthivhi, Fulufhelo Faith. <i>"Assessing the spatiotemporal variation of phytoplankton biomass in Nandoni reservoir in the Vhembe District (South Africa) using LANDSAT satellite imagery."</i> ., , 2022. http://hdl.handle.net/11602/2295TY - Dissertation AU - Muthivhi, Fulufhelo Faith AB - Chlorophyll–a (chl–a) is an optical active compound used as proxy for phytoplankton biomass to determine the trophic state of the aquatic ecosystem. Traditional approaches for monitoring aquatic system are time consuming, expensive, and non–continuous, therefore, Remote Sensing technologies are qualitative for monitoring the status for water quality in large scale and low cost. The aim of this study was to assess the spatial and temporal variation of phytoplankton biomass in Nandoni reservoir, Limpopo to examine the relationship that exist between the physico–chemical variables and chl–a concentration using readily available Landsat multispectral images. Multispectral resolution of (30 m) Landsat 7 ETM+ and Landsat 8 OLI images for June to December 2008 to 2020 were used to derive the distribution of chl–a concentration. The spatial distribution of chl–a concentration in wet and dry season of these years was obtained. By using regression techniques, in situ measured chl–a was related to construct and validate Landsat predicted chl–a to determine the distribution of chl–a in the reservoir. The results indicate that Landsat derived chl–a was similar with the observed measured chlorophyll–a (R2 = 0.91). There was a negative significant correlation among Land Use and Land Cover with water quality (P > 0.05). Using permutational multivariate analysis of variance (PERMANOVA) analysis, there was significant differences for chl–a concentration in sites, seasons, and zones. There was positive significant correlation observed on water temperature with strong negative significant with salinity and TDS. A strong perfect linear association among predicted vs measured chl–a were found. Chlorophyll–a concentration in Nandoni reservoir was derived using Landsat remote sensing images, suggesting that Landsat sensor is suitable for monitoring small reservoir in a short timescale. Remote sensing techniques can be used to control the development of an early warning system of this study and other reservoirs. DA - 2022-07-15 DB - ResearchSpace DP - Univen LK - https://univendspace.univen.ac.za PY - 2022 T1 - Assessing the spatiotemporal variation of phytoplankton biomass in Nandoni reservoir in the Vhembe District (South Africa) using LANDSAT satellite imagery TI - Assessing the spatiotemporal variation of phytoplankton biomass in Nandoni reservoir in the Vhembe District (South Africa) using LANDSAT satellite imagery UR - http://hdl.handle.net/11602/2295 ER -