Browsing by Author "Matthews, Mark"

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    Open Access
    Monitoring eutrophication in the Vaal Dam using satellite remote sensing
    (2018) Hlahane, Keneilwe; Reason, Chris; Matthews, Mark
    The Vaal Dam is one of South Africa’s important inland water resources, however it is experiencing ecological problems related to eutrophication. The dam supplies water for domestic, industrial, mining and agricultural usage. This research aims to assess and monitor the threats of eutrophication and cyanobacterial blooms within the Vaal Dam. A 10-year archive of remotely sensed satellite data was collected from the medium resolution imaging spectrometer full resolution (MERIS FR) satellite from 2002 to 2012. Data products on chlorophyll-a, cyanobacteria concentration, cyanobacteria and surface scum percentage coverage data were derived from MERIS satellite imagery, using the maximum peak height (MPH) algorithm. The derived data products were used as indicator measures of the trophic status. This research presents a time series analysis of chlorophyll-a (a proxy for eutrophication) and cyanobacteria to establish the status, seasonality and trends for the Vaal Dam. Statistical analysis methods were applied to determine the drivers of eutrophication. In addition, the effects of climate variables on eutrophication were analyzed. Geographic Information Systems methods were applied to determine the spatial distribution and variations of chlorophyll- a. The results indicate the trophic status of the Vaal Dam ranged from being eutrophic and hypertrophic over the 10-year period. Seasonality analysis indicated that cyanobacteria blooms increased in production during the summer period and decreased in winter. Statistical analysis of the results indicated that the correlation between Chl-a and nutrients is not statistically significant. Therefore, nutrients themselves are not driving eutrophication in the Vaal Dam. The produced maps from satellite images showed the spatial distribution of Chl-a within dam. The maps indicated the eastern areas of the Vaal Dam as areas where algal and cyanobacteria blooms occur in high concentrations. The correlation between Chl-a and climate variables indicates that there is a correlation with temperature and wind speed, and an indistinct relationship with rainfall. The study concludes that both nutrient and climatic variables contribute as drivers of eutrophication within the Vaal Dam. The methods applied in this research will help to transform the satellite data into useful knowledge products, which can be used to supplement current monitoring of inland freshwater resources.
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    Towards high fidelity mapping of global inland water quality using earth observation data
    (2021) Kravitz, Jeremy; Matthews, Mark; Bernard, Stewart; Fawcett, Sarah
    This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality.
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    The use of reflectance classification for chlorophyll algorithm application across multiple optical water types in South African coastal waters
    (2016) Smith, Marié; Vichi, Marcello; Bernard, Stewart; Matthews, Mark
    Ocean colour remote sensing is a valuable tool for deriving information about key biogeochemical variables over inland, coastal and ocean waters at scales unachievable via in situ techniques. However, broader use of ocean colour data is still limited by the need for users to choose among a seemingly complicated range of available satellite products and to understand the limitations and constraints of these products across a wide range of water types. This issue could benefit from the capability to seamlessly apply and blend watertype appropriate algorithms into a single output product that provides optimal retrievals over a wide range of water types. The assessment of the fuzzy membership of satellite remote sensing reflectance (Rᵣₛ) to pre-defined regional optical water types (OWTs) provides a framework for application and blending of OWT-appropriate algorithms on a per-pixel basis. This study presents the first characterization of the OWTs in the coastal waters of South Africa. The OWTs are determined through stepwise fuzzy c-means clustering of a systematically expanding and modified database constructed from in situ, synthetic and regionally extracted Medium Resolution Imaging Spectrometer (MERIS) Rᵣₛ. A database division allows separate and more detailed clustering of phytoplankton-dominated Rᵣₛ and backscattering-dominated Rᵣₛ into six and five classes respectively. Chlorophyll α (Chl α) algorithms are assigned per OWT based on lowest error and uncertainty. The blended Chl α product consists of weighted retrievals from five different algorithms, including two 4th order polynomial exponential algorithms utilizing the blue-green spectral region, two red-NIR band ratio algorithms, and a neural network. The algorithm blending procedure retrieves satellite-derived Chl α concentration ([Chl α]) with lower RMS error and uncertainty compared to individual algorithms and provides improved capability to retrieve [Chl α] for different South African water types with a single product over a range spanning almost four orders of magnitude. The eleven OWTs are utilized in the classification and algorithm blending framework and applied to the full archive of MERIS Level 2 reflectance between the years 2002 and 2012 over South Africa's coastal waters. The persistence of the OWTs is presented and linked to the prominent environmental and physical drivers, whilst regions with low total class membership sums are discussed in terms of satellite data coverage and data quality. A time series of the blended [Chl α] product displays improved capability to capture the ranges of variability observed in the coastal, shelf and offshore environment compared to currently available regional and standard MERIS Level 2 products.