Linking phytoplankton pigment composition and optical properties: A framework for developing remote-sensing metrics for monitoring cyanobacteria

International audienceThis study has been performed in the framework of a research program aiming to develop a low-cost aerial sensor for the monitoring of cyanobacteria in freshwater ecosystems that could be used for early detection. Several empirical and mechanistic remote-sensing tools have been already developed and tested at large scales and have proven useful in monitoring cyanobacterial blooms. However, the effectiveness of these tools for early detection is hard to assess because such work requires the detection of low concentrations of characteristic pigments amid complex ecosystems exhibiting several confounding factors (turbidity, blooms of other species, etc.). We developed a framework for performing high-throughput measurements of the absorbance and reflectance of small volumes (~= 20 mL) of controlled mixtures of phytoplankton species and studied the potential of this framework to validate remote-sensing proxies of cyanobacteria concentration. The absorption and reflectance spectra of single and multiple cultures carried a specific signal that allowed for the quantitative analysis of culture mixes. This specific signal was shown to be related to known pigment absorbance spectra. The concentrations of chlorophyll-a and -b, phycocyanin and phycoerythrin could be obtained from direct absorbance measurements and were correlated with the concentration obtained after pigment extraction (R2 ≥ 0.96 for all pigments). A systematic test of every possible two-band and three-band normalized difference between optical indices was then performed, and the coincidental correlation with chlorophyll-b (absent in cyanobacteria) was used as an indicator of non-specificity. Two-band indices were shown to suffer from non-specificity issues and could not yield strong and specific relationships with phycocyanin or phycoerythrin (maximum R2  0.8)

Using Remote Sensing and Environmental Precursors to Detect and Predict Cyanobacteria Harmful Algal Blooms in Northeastern US Waterbodies

Cyanobacteria harmful algal blooms (cyanoHABs) are a global problem with human health, environmental, and economic concerns. The severity and frequency of toxic cyanoHABs are expected to increase with climate change. Remote sensing has proven to be a useful tool in monitoring cyanoHABs. This study uses remote sensing observations from Sentinel-3Ocean Land Color Imager (OLCI)combined with the Spectral Shape Algorithm (SSA) to detect the presence of cyanobacteria in numerous waterbodies throughout the Northeast United States over 2016to 2020. The ACOLITE processor was used for the atmospheric correction of the Sentinel-3 OLCI data, as it has been shown to provide more accurate results over water bodies. Citizen scientist data from two EPA apps, BloomWatch and CyanoScope, were used to guide the cyanoHAB detection results. This study focuses on three different aspects of using the remote sensing methods to detect cyanobacteria harmful algal blooms: (1) examine two different reflectance center band combinations of the SSA at 665 nm and 681 nm, (2) compare SSA controls for non-bloom periods in winter/spring to the summer bloom periods, and (3) examine bloom areas within waterbodies. The SSA method with the center band at 665 nm was found to perform better than the SSA method with the center band at 681 nm. Additionally, cyanoHAB environmental precursors were determined and their effects on the formation of cyanoHABs were assessed, with maximum air temperature having the greatest impact on cyanoHABs formation

Evaluating and Predicting the Risk of Algal Blooms in a Freshwater Lake through a 4-Dimensional Approach: A Case Study on Lake Mitchell

Excessive algal growth in freshwater lakes can negatively impact ecosystems, recreation, and human health. Though algae are a natural part of freshwater ecosystems, elevated nutrient loading from anthropogenic and natural sources can lead to algal blooms. Both algae and blue-green algae (BGA) are responsible for algal blooms; however, BGA (cyanobacteria) is more dangerous. The first objective of this research was to prepare a conceptual model to understand how various environmental variables affect algae. This conceptual model was used to choose the environmental variables that help increase or decrease algae in the water environment. The second objective was to develop empirical equations to identify how the environmental variables are helping algal increase or decrease. Lake Mitchell, near Mitchell, SD, was chosen as a case study to collect the data of the environmental variables. Along with the total algae (Total algae = Chlorophyll-a + Phycocyanin), five variables: (1) conductivity, (2) temperature, (3) fluorescent dissolved organic matter, (4) ammonium, and (5) dissolved oxygen, were collected. Algae concentrations can change temporally, vertically within the water column, and spatially across lakes and thus, a four-dimensional approach was used to accurately quantify alga

Removal of Chlorophyll-a Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance

Monitoring 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

The development and application of a charge- coupled device based instrument for at-site monitoring of algae and cyanobacteria in freshwaters

A thesis submitted for the degree of Doctor of Philosophy of the University of LutonThe research presented in this thesis describes the development and application of a portable, high-resolution instrument, specifically designed for the at-site monitoring of algae and cyanobacteria in freshwaters. The instrument incorporates a miniature charge-coupled device (CCD) based spectrometer and a low power combined deutelium and tungsten light source, enabling the absorbance to be measured between 200 - 850 nm at a resolution of 1.3 nm. A transmission dip probe with removable tips of 5, 10 and 40 mm pathlengths forms the sampling device. A specifically developed control program allows easy operation of the instrument. A linear response from 0.0 - 1.2 AU and a combined signal to noise ratio of 576: 1 for the instrument components resulting in a high baseline stability of 1.0 mAU drift over five hundred measurements being observed. The instrument provides in-vivo absorbance characteristics with high resolution across the visible spectrum. Up to twelve specific spectral features were commonly identified in the absorbance spectra of algae and cyanobacteria between 400 - 750 nm. Individual spectral features were linked to specific pigments, some of which were found to be taxonomically distinct. Fourth derivative analysis was proven to provide further enhancement of subtle spectral features. The instrument has a linear range for chlorophyll a up to 1000 !lg rl and a detection limit of 8 )lg rl using the 40 mm pathlength probe. Physiological adaptation to light and nutrient conditions were shown to have a significant effect on the in-vivo absorbance spectrum, therefore providing potential information on physiological status and health of a natural sample. Spectral analysis using principal component analysis (PCA) with classification based on the soft independent modelling of class analogy (SIMCA) method was used to classify nine species from three taxonomic classes, including four cyanobacteria (Microcystis aeruginosa, Anabaena variabilis, Aphanizornenon flos-aquae, Synechnococcus sp.), four chlorophyceae (Chlorella vulgaris, Scenedesmus acuminatus, Spirogyra mirabilis, Staurastrurn chaetoceros) and a single bacillariophyceae (Asterionella Formosa). Classification using the SIMCA method proved to be highly reliable and robust. Moreover, the addition of noise was found to have very little effect on the classification. Under laboratory conditions all nine species were correctly classified using 'unknown' spectra. At-site classification of natural samples and laboratory simulations have shown the robustness and reliability of the developed portable instrument. In combination with the data analysis techniques, the instrument is well suited to the proactive at-site assessment of algal and cyanobacterial blooms in eutrophic freshwater environments

Detecting the Spatial Patterns of Blue-green Algae in Harsha Lake using Landsat 8 Imagery

The incidence of harmful algal blooms (HABs) caused by blue-green algae has been increasing in coastal and freshwater ecosystems of the United States in recent years, and has had great influence on ecosystem, economic, and public health. This thesis aims at testing the feasibility of using machine learning methods in comparison to traditional regression models to detect and map the blue-green algae distribution in low-medium biomass waters (Chl-a \u3c approx. 20 μg/L) from a Landsat 8 image with the support of some in situ Chl-a measurements in Harsha Lake, Ohio. Two algorithms were compared: one is the conventional empirical method – Stepwise Multiple Linear Regression – to see if there is a strong linear relationship between measured Chl-a concentrations and the Landsat 8 spectral data in the study area, and the other is one of the most popular machine learning methods–Random Forests. Major findings include: (1) both a conventional linear regression model and a Random Forests model worked well in mapping the extent and biomass of blue-green algae in Harsha Lake on September 21, 2015, but the Random Forests model outperformed the linear regression model; (2) the prediction surface from the Random Forests method illustrated that 89.30% of Harsha Lake’s area had Chl-a values less than 10 µg/L on the sampling date, while only 10.70% of the entire study area had Chl-a concentrations between 10 µg/L and 20 µg/L. Higher Chl-a values (especially for Chl-a larger than 10 µg/L) were mostly distributed in the mouths of rivers or streams, which might be caused by the influx of nutrients from agricultural or urban land use by rivers and streams. The results show the utility of the Random Forests approach based on Landsat 8 imagery in detecting and quantitatively mapping low biomass HABs, which is considered to be a challenging task

Remote sensing and bio-geo-optical properties of turbid, productive inland waters: a case study of Lake Balaton

Algal blooms plague freshwaters across the globe, as increased nutrient loads lead to eutrophication of inland waters and the presence of potentially harmful cyanobacteria. In this context, remote sensing is a valuable approach to monitor water quality over broad temporal and spatial scales. However, there remain several challenges to the accurate retrieval of water quality parameters, and the research in this thesis investigates these in an optically complex lake (Lake Balaton, Hungary). This study found that bulk and specific inherent optical properties [(S)IOPs] showed significant spatial variability over the trophic gradient in Lake Balaton. The relationships between (S)IOPs and biogeochemical parameters differed from those reported in ocean and coastal waters due to the high proportion of particulate inorganic matter (PIM). Furthermore, wind-driven resuspension of mineral sediments attributed a high proportion of total attenuation to particulate scattering and increased the mean refractive index (n̅p) of the particle assemblage. Phytoplankton pigment concentrations [chlorophyll-a (Chl-a) and phycocyanin (PC)] were also accurately retrieved from a times series of satellite data over Lake Balaton using semi-analytical algorithms. Conincident (S)IOP data allowed for investigation of the errors within these algorithms, indicating overestimation of phytoplankton absorption [aph(665)] and underestimation of the Chl-a specific absorption coefficient [a*ph(665)]. Finally, Chl-a concentrations were accurately retrieved in a multiscale remote sensing study using the Normalized Difference Chlorophyll Index (NDCI), indicating hyperspectral data is not necessary to retrieve accurate pigment concentrations but does capture the subtle heterogeneity of phytoplankton spatial distribution. The results of this thesis provide a positive outlook for the future of inland water remote sensing, particularly in light of contemporary satellite instruments with continued or improved radiometric, spectral, spatial and temporal coverage. Furthermore, the value of coincident (S)IOP data is highlighted and contributes towards the improvement of remote sensing pigment retrieval in optically complex waters

Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms

Harmful cyanobacteria blooms have been increasing in frequency throughout the world resulting in a greater need for water quality monitoring. Traditional methods of monitoring water quality, such as point sampling, are often resource expensive and time consuming in comparison to remote sensing approaches, however the spatial resolution of established water remote sensing satellites is often too coarse (300 m) to resolve smaller inland waterbodies. The fine scale spatial resolution and improved radiometric sensitivity of Landsat satellites (30 m) can resolve these smaller waterbodies, enabling their capability for cyanobacteria bloom monitoring. In this work, the utility of Landsat to retrieve concentrations of two cyanobacteria bloom pigments, chlorophyll-a and phycocyanin, is assessed. Concentrations of these pigments are retrieved using a spectral Look-Up-Table (LUT) matching process, where an exploration of the effects of LUT design on retrieval accuracy is performed. Potential augmentations to the spectral sampling of Landsat are also tested to determine how it can be improved for waterbody constituent concentration retrieval. Applying the LUT matching process to Landsat 8 imagery determined that concentrations of chlorophyll-a, total suspended solids, and color dissolved organic matter were retrieved with a satisfactory accuracy through appropriate choice of atmospheric compensation and LUT design, in agreement with previously reported implementations of the LUT matching process. Phycocyanin proved to be a greater challenge to this process due to its weak effect on waterbody spectrum, the lack of Landsat spectral sampling over its predominant spectral feature, and error from atmospheric compensation. From testing potential enhancements to Landsat spectral sampling, we determine that additional spectral sampling in the yellow and red edge regions of the visible/near-infrared (VNIR) spectrum can lead to improved concentration retrievals. This performance further improves when sampling is added to both regions, and when Landsat is transitioned to a VNIR imaging spectrometer, though this is dependent on band position and spacing. These results imply that Landsat can be used to monitor cyanobacteria blooms through retrieval of chlorophyll-a, and this retrieval performance can be improved in future Landsat systems, even with minor changes to spectral sampling. This includes improvement in retrieval of phycocyanin when implementing a VNIR imaging spectrometer

Fluorescence properties of Baltic Sea phytoplankton

To obtain data on phytoplankton dynamics (abundance, taxonomy, productivity, and physiology) with improved spatial and temporal resolution, and at reduced cost, traditional phytoplankton monitoring methods have been supplemented with optical approaches. Fluorescence detection of living phytoplankton is very sensitive and not disturbed much by the other optically active components. Fluorescence results are easy to generate, but interpretation of measurements is not straightforward as phytoplankton fluorescence is determined by light absorption, light reabsorption, and quantum yield of fluorescence - all of which are affected by the physiological state of the cells. In this thesis, I have explored various fluorescence-based techniques for detection of phytoplankton abundance, taxonomy and physiology in the Baltic Sea.In algal cultures used in this thesis, the availability of nitrogen and light conditions caused changes in pigmentation, and consequently in light absorption and fluorescence properties of cells. The variation of absorption and fluorescence properties of natural phytoplankton populations in the Baltic Sea was more complex. Physical environmental factors (e.g. mixing depth, irradiance and temperature) and related seasonal succession in the phytoplankton community explained a large part of the seasonal variability in the magnitude and shape of Chlorophyll a (Chla)-specific absorption. Subsequent variations in the variables affecting fluorescence were large; 2.4-fold for light reabsorption at the red Chla peak and 7-fold for the spectrally averaged Chla-specific absorption coefficient for Photosystem II. In the studies included in this thesis, Chla-specific fluorescence varied 2-10 fold. This variability in Chla-specific fluorescence was related to the abundance of cyanobacteria, the size structure of the phytoplankton community, and absorption characteristics of phytoplankton.Cyanobacteria show very low Chla-specific fluorescence. In the presence of eukaryotic species, Chla fluorescence describes poorly cyanobacteria. During cyanobacterial bloom in the Baltic Sea, phycocyanin fluorescence explained large part of the variability in Chla concentrations. Thus, both Chla and phycocyanin fluorescence were required to predict Chla concentration.Phycobilins are major light harvesting pigments for cyanobacteria. In the open Baltic Sea, small picoplanktonic cyanobacteria were the main source of phycoerythrin fluorescence and absorption signal. Large filamentous cyanobacteria, forming harmful blooms, were the main source of the phycocyanin fluorescence signal and typically their biomass and phycocyanin fluorescence were linearly related. It was shown that for reliable phycocyanin detection, instrument wavebands must match the actual phycocyanin fluorescence peak well. In order to initiate an operational ship-of-opportunity monitoring of cyanobacterial blooms in the Baltic Sea, the distribution of filamentous cyanobacteria was followed in 2005 using phycocyanin fluorescence.Various taxonomic phytoplankton pigment groups can be separated by spectral fluorescence. I compared multivariate calibration methods for the retrieval of phytoplankton biomass in different taxonomic groups. During a mesocosm experiment, a partial least squares regression method gave the closest predictions for all taxonomic groups, and the accuracy was adequate for phytoplankton bloom detection. This method was noted applicable especially in the cases when not all of the optically active compounds are known.Variable fluorescence has been proposed as a tool to study the physiological state of phytoplankton. My results from the Baltic Sea emphasize that variable fluorescence alone cannot be used to detect nutrient limitation of phytoplankton. However, when combined with experiments with active nutrient manipulation, and other nutrient limitation indices, variable fluorescence provided valuable information on the physiological responses of the phytoplankton community. This thesis found a severe limitation of a commercial fast repetition rate fluorometer, which couldn’t detect the variable fluorescence of phycoerythrin-lacking cyanobacteria. For these species, the Photosystem II absorption of blue light is very low, and fluorometer excitation light did not saturate Photosystem II during a measurement.This thesis encourages the use of various in vivo fluorescence methods for the detection of bulk phytoplankton biomass, biomass of cyanobacteria, chemotaxonomy of phytoplankton community, and phytoplankton physiology. Fluorescence methods can support traditional phytoplankton monitoring by providing continuous measurements of phytoplankton, and thereby strengthen the understanding of the links between biological, chemical and physical processes in aquatic ecosystems