Browsing by Subject "phycocyanin"
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Item Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery(Elsevier, 2021-12) O'Shea, Ryan E.; Pahlevan, Nima; Smith, Brandon; Bresciani, Mariano; Egerton, Todd; Giardino, Claudia; Li, Lin; Moore, Tim; Ruiz-Verdu, Antonio; Ruberg, Steve; Simis, Stefan G. H.; Stumpf, Richard; Vaičiūtė, Diana; Earth Sciences, School of ScienceRetrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency's PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC estimation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (<10 mg m−3) PC. Visibly, HICO and PRISMA PC maps show the wider dynamic range that can be represented by the MDN. The available in situ and satellite-derived Rrs matchups and measured in situ PC demonstrate the robustness of the MDN for estimating low (<10 mg m−3) PC and the reduced impact of ∆Rrs on medium-to-high in situ PC (>10 mg m−3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales.Item CONFOUNDING CONSTITUENTS IN REMOTE SENSING OF PHYCOCYANIN(2008-08-22T13:53:38Z) Vallely, Lara Anne; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, LinThis project examines the impact of confounding variables that have limited the accuracy of remotely predicting phycocyanin in three Indiana drinking and recreational water reservoirs. In-situ field reflectance spectra were collected from June to November 2006 over a wide range of algal bloom conditions using an ASD Fieldspec (UV/VNIR) spectroradiometer. Groundtruth samples were analyzed for chlorophyll a, phycocyanin, total suspended matter, and other water quality constituents. Previously published spectral algorithms for the detection of phycocyanin were evaluated against lab measured pigment concentrations using linear least squares regression. Algorithm performance varied across study sites (best performing models by reservoir resulted in r2 values of 0.32 to 0.84). Residuals of predicted versus measured pigment concentrations were analyzed against concentration of potential confounding water constituents. Residual analysis revealed optically active constituents contributed between 25% and 95% of original phycocyanin model errors. Inclusion of spectral variables into models to account for significant confounders resulted in improved spectral estimates of phycocyanin (r2 = 0.56 to 0.93).Item Improving Remote Sensing Algorithms Towards Inland Water Cyanobacterial Assessment From Space(2021-09) Ogashawara, Igor; Li, Lin; Moreno-Madriñán, Max Jacobo; Druschel, Gregory K.; Hwang, Taehee; Wang, LixinWater is an essential resource for life on Earth, and monitoring its quality is an important task for mankind. However, the amount of water quality data collected by the traditional method is insufficient for the conservation and sustainable management of this important resource. This challenge will be exacerbated by increasing harmful algal blooms at the global scale. To fill this gap, Earth Observations (EO) have been proposed to help stakeholders make their decisions, but the use of EO for monitoring inland water quality is still in development. In this context, the main objective of this study was to improve the estimation of cyanobacteria via remote sensing data. To achieve this goal, the water type classification was first used to identify the dominant optically active constituents within aquatic environments. This information is crucial for understanding the optical properties of inland waters and selecting the best remote sensing algorithm for specific optical water types. The next research question was to develop a universal structure for retrieval of the inherent optical properties of several important aquatic systems around the world, which can be used as a corner stone for developing a globally applicable remote sensing algorithm. The third research topic of this dissertation is about removing the interference of chlorophyll-a with the absorption strength at 620 nm where phycocyanin exhibits its diagnostic absorption so that the estimation of phycocyanin concentration can be improved. Despite the novelty of the proposed remote sensing algorithms which are able to accommodate distinct water optical properties, there are abundant opportunities for improving the parameterization of the proposed models to retrieve inland water quality and optical properties when a global database of optical and water quality measurements is available. Considering the current advancement in spaceborne technology and the existence of a coordinate effort for global calibration and validation of remote sensing algorithms for monitoring inland waters, there is a high potential for operational assessment of harmful cyanobacterial blooms using the remote sensing algorithms proposed in this dissertation.Item Monitoring Phycocyanin with Landsat 8/Operational Land Imager Orange Contra-Band(MDPI, 2022-03-19) Ogashawara, Igor; Li, Lin; Howard, Chase; Druschel, Gregory K.; Earth and Environmental Sciences, School of ScienceThe Operational Land Imager (OLI) onboard the Landsat 8 satellite has a panchromatic band (503–676 nm) that has been used to compute a virtual spectral band known as “orange contra-band” (590–635 nm). The major application of the orange contra-band is the monitoring of cyanobacteria which is usually quantified by the measurement of the concentration of phycocyanin (PC) which has an absorption peak around 620 nm. In this study, we evaluated the use of the orange contra-band approach for estimating PC concentration from in situ proximal hyperspectral data from Eagle Creek Reservoir (ECR), in Indiana, USA. We first validated the empirical relationship for the computation of the orange contra-band by using the panchromatic, red, and green spectral bands from ECR. PC concentration retrieval using the orange contra-band were not successful when using the entire dataset (R2 < 0.1) or when using only PC concentrations higher than 50 mg/m3 (R2 < 0.24). Better results were achieved when using samples in which PC was 1.5 times higher than the chlorophyll-a concentration (R2 = 0.84). These results highlighted the need for the development of remote sensing algorithms for the accurate estimation of PC concentration from non-PC dominant waters which could be use to track and/or predict cyanobacteria blooms.Item Removal of Chlorophyll-a Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance(MDPI, 2019-08) Ogashawara, Igor; Li, Lin; Earth Sciences, School of ScienceMonitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-a (chl-a) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-a contribution to the absorption at 620 nm based on empirical relationships established using chl-a and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 µg/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.Item The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms(MDPI, 2019-06) Ogashawara, Igor; Earth Sciences, School of ScienceCyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed.Item Using Band Ratio, Semi-Empirical, Curve Fitting, and Partial Least Squares (PLS) Models to Estimate Cyanobacterial Pigment Concentration from Hyperspectral Reflectance(2009-09-03T15:01:53Z) Robertson, Anthony Lawrence; Li, Lin; Tedesco, Lenore P.; Wilson, Jeffrey S. (Jeffrey Scott), 1967-This thesis applies several different remote sensing techniques to data collected from 2005 to 2007 on central Indiana reservoirs to determine the best performing algorithms in estimating the cyanobacterial pigments chlorophyll a and phycocyanin. This thesis is a set of three scientific papers either in press or review at the time this thesis is published. The first paper describes using a curve fitting model as a novel approach to estimating cyanobacterial pigments from field spectra. The second paper compares the previous method with additional methods, band ratio and semi-empirical algorithms, commonly used in remote sensing. The third paper describes using a partial least squares (PLS) method as a novel approach to estimate cyanobacterial pigments from field spectra. While the three papers had different methodologies and cannot be directly compared, the results from all three studies suggest that no type of algorithm greatly outperformed another in estimating chlorophyll a on central Indiana reservoirs. However, algorithms that account for increased complexity, such as the stepwise regression band ratio (also known as 3-band tuning), curve fitting, and PLS, were able to predict phycocyanin with greater confidence.