Monitoring Algal Abundance and Water Quality in Arizona Reservoirs Through Field Sampling and Remote Sensing

abstract: Safe, readily available, and reliable sources of water are an essential component of any municipality’s infrastructure. Phoenix, Arizona, a southwestern city, has among the highest per capita water use in the United States, making it essential to carefully manage its reservoirs. Generally, municipal water bodies are monitored through field sampling. However, this approach is limited spatially and temporally in addition to being costly. In this study, the application of remotely sensed reflectance data from Landsat 7’s Enhanced Thematic Mapper Plus (ETM+) and Landsat 8’s Operational Land Imager (OLI) along with data generated through field-sampling is used to gain a better understanding of the seasonal development of algal communities and levels of suspended particulates in the three main terminal reservoirs supplying water to the Phoenix metro area: Bartlett Lake, Lake Pleasant, and Saguaro Lake. Algal abundances, particularly the abundance of filamentous cyanobacteria, increased with warmer temperatures in all three reservoirs and reached the highest comparative abundance in Bartlett Lake. Prymnesiophytes (the class of algae to which the toxin-producing golden algae belong) tended to peak between June and August, with one notable peak occurring in Saguaro Lake in August 2017 during which time a fish-kill was observed. In the cooler months algal abundance was comparatively lower in all three lakes, with a more even distribution of abundance across algae classes. In-situ data from March 2017 to March 2018 were compared with algal communities sampled approximately ten years ago in each reservoir to understand any possible long-term changes. The findings show that the algal communities in the reservoirs are relatively stable, particularly those of the filamentous cyanobacteria, chlorophytes, and prymnesiophytes with some notable exceptions, such as the abundance of diatoms, which increased in Bartlett Lake and Lake Pleasant. When in-situ data were compared with Landsat-derived reflectance data, two-band combinations were found to be the best-estimators of chlorophyll-a concentration (as a proxy for algal biomass) and total suspended sediment concentration. The ratio of the reflectance value of the red band and the blue band produced reasonable estimates for the in-situ parameters in Bartlett Lake. The ratio of the reflectance value of the green band and the blue band produced reasonable estimates for the in-situ parameters in Saguaro Lake. However, even the best performing two-band algorithm did not produce any significant correlation between reflectance and in-situ data in Lake Pleasant. Overall, remotely-sensed observations can significantly improve our understanding of the water quality as measured by algae abundance and particulate loading in Arizona Reservoirs, especially when applied over long timescales.Dissertation/ThesisMasters Thesis Sustainability 201

CHARACTERIZING FRESHWATER PHYTOPLANKTON DYNAMICS WITH ELECTRO-OPTICAL REMOTE SENSING

Freshwater lakes are an important component contributing to ecosystem health and biodiversity on local, regional, and global scales. And while lakes only represent \u3c5% of the global surface area, they are often very productive systems which contribute significantly to carbon cycling dynamics and freshwater fish production on a number of spatial scales. Due to the remote location and sheer size of some of these lakes it has proven difficult to adequately document changes in water quality. Significant challenges exist to adequately monitor water quality, and in particular phytoplankton dynamics, over large spatial and temporal scales using traditional in situ methods. Satellite electro-optical remote sensing offers a potential tool to provide better characterization of phytoplankton dynamics for a variety of freshwater systems. This work resulted in an approach to quantify global summer phytoplankton abundance using a newly developed remote sensing derived chlorophyll-a product. This product was also used in conjunction with a newly created carbon fixation model to assess global freshwater phytoplankton production which provided new insights into the role freshwater systems play in the global carbon budget. Spatial and temporal assessments of specific populations of phytoplankton and cyanobacteria were established through the development of a new remote sensing algorithm to isolate high biomass assemblages in the Laurentian Great Lakes (Lake Erie, Lake Huron, Lake Michigan). The algorithm was developed to facilitate the fusion of multiple remote sensing data sources (SeaWiFS and MODIS) in order to generate a new 20-year time-series data product to better understand the factors controlling bloom dynamics. Finally, a spatio-temporal analysis documenting the variability of inherent optical properties (IOPs) in Lake Erie established a seasonal progression of phytoplankton/cyanobacteria community structures for two years over the vegetative season, the findings of which are critical for the development of the next generation of hyperspectral remote sensing algorithms to improve phytoplankton community characterizations from space. These documented results clearly show the utility of electro-optical remote sensing to provide characterization of phytoplankton dynamics and insights at both community and population scales in freshwater systems

Multitemporal spectral analysis for algae detection in an eutrophic lake using Sentinel 2 images

Eutrophication is characterized by excessive plant and algal growth due to the increased of organic matter, carbon dioxide and nutrients in water body. Although eutrophication naturally occurs over centuries as lakes age, human activities have accelerated it processes and caused dramatic changes to the aquatic ecosystems including elevated algae blooms and risk for hypoxia as well as degradation in the quality of drinking water and fisheries. Monitoring eutrophic processes is therefore highly important to human health and to the aquatic environment. However, the spatial and seasonal distribution of the phenomena and its dynamic are difficult to be resolved using conventional methods as water sampling or sparse acquisition of remote sensing data. This research work proposes a methodology that takes advantage of the high temporal resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced temporal unmixing model to estimate the abundance of [Chl-a] and algae species in San Roque reservoir, Argentina, in the period August 2016 to August 2019. The spatial patterns and the temporal tendencies of these aquatic indicators, that have a direct link to Eutrophication, were analysed and evaluated using in situ data in order to assess their contribution to the local water management.Fil: German, Alba. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Ferral, Anabella. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Scavuzzo, Carlos Matias. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Shimoni, M.. Belgian Royal Military Academy; Bélgic

Quantification of Harmful Algal Blooms in Multiple Water Bodies of Mississippi Using In-Situ, Analytical and Remote Sensing Techniques

Globally, water bodies are increasingly affected by undesirable harmful algal blooms. This dissertation contributes to research methodology pertaining to quantification of the algal blooms in multiple water bodies of Mississippi using in situ, analytical, and remote sensing techniques. The main objectives of this study were to evaluate the potential of several techniques for phytoplankton enumeration and to develop remote sensing algorithms for several sensors and evaluate the performance of the sensors for quantifying phytoplankton in several water bodies. Analytical techniques such as “FlowCam”, an imaging flow cytometer; “HPLC”, high performance liquid chromatography with the chemical taxonomy program “ChemTax”; spectrofluorometric analyses; and “ELISA” assay were used to quantify a suite of parameters on algal blooms. Additionally, in-situ algal pigment biomass was measured using fluorescence probes. It was found that that each technique has unique potential. While some of the rapid and simpler techniques can be used instead of more involved techniques, sometimes use of several techniques together is beneficial for managing aquatic ecosystems and protecting human health. Algorithms were developed to quantify chlorophyll a using five remote sensing sensors including three currently operational satellite sensors and two popular sensors onboard the Unmanned Aerial Systems (UASs). Empirical band ratio algorithms were developed for each sensor and the best algorithms were chosen. Cluster analysis helped in differentiating the water types and linear regression was used to develop algorithms for each of the water types. The UAS sensor- Micasense was found to be most useful among the UAS sensors and the best overall with highest R2 value 0.75 with p\u3c0.05 and minimum %RMSE of 28.22% and satellite sensor OLCI was found to be most efficient among the three satellite sensors used in the study for chlorophyll a estimation with R2 of 0.75 with p\u3c0.05 and %RMSE 13.19%. The algorithms developed for these sensors in this study represent the best algorithms for chlorophyll a estimation in these water bodies based on R2 and %RMSE. The applicability of the algorithms can be extended to other water bodies directly or the approach developed in this study can be adopted for estimating Chl a in other water bodies

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

Characterization of harmful algal bloom frequency, severity, and spatial extent in Oklahoma reservoirs utilizing the cyanobacteria assessment network

Harmful algal blooms (HABs) pose significant threats to human health and the environment. Monitoring them in inland waterbodies is a challenging and costly task. Remote sensing technology is an increasingly useful tool in monitoring and managing HABs, providing timely information on their bloom dynamics. The Cyanobacteria Assessment Network (CyAN) was developed to provide a consistent and uniform program for HAB detection and characterization across the United States. CyAN utilizes the Ocean and Land Colour Imagers (OLCI) aboard Sentinel 3A and 3B to provide near daily imagery of the major waterbodies across the country. The objective of this study is to characterize the frequency, spatial extent, and severity of HABs in Oklahoma Reservoirs utilizing CyAN. Sixty nine waterbodies were selected for analysis. They include the largest lakes and reservoirs in Oklahoma. Frequency, spatial extent, and severity were assessed for trends over the six-year study period (2017-2022). Trend analysis was grouped into four bloom categories: high (>100,000 cells/ml), medium (20,000-100,000 cells/ml), low (<20,000 cells/ml), and total bloom. High, medium, and low-risk bloom thresholds are based off of the World Health Organizations risk of health impact thresholds. Total blooms represent any bloom level above sensor detection. The findings of this research indicate that statewide HABs are increasing in frequency over the study period for all bloom categories. The spatial extent of HABs is increasing statewide for all bloom risk categories. Bloom severity is increasing for multiple individual waterbodies. Significant differences in bloom frequency, severity, and spatial extent are observed between trophic states. The findings of this research highlight the potential of remote sensing as a valuable tool for HAB monitoring and provide insights for developing effective HAB management strategies

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

Towards high fidelity mapping of global inland water quality using earth observation data

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

Development of bio-optical algorithms to estimate chlorophyll in the Great Salt Lake and New England lakes using in situ hyperspectral measurements

Chlorophyll is widely used to evaluate lake water quality, effectively integrating the chemical, physical and biological state of a lake. Assessment of chlorophyll conditions in lakes can be greatly enhanced by the use of remote sensing, allowing information to be gathered at spatial and temporal scales not possible with traditional limnological sampling methods. In order for remote sensing methods to provide accurate estimates of chlorophyll concentration, algorithms need to be developed with high-quality spectral data paired with water quality measurements and optimized for regional lake differences. In this study, in situ hyperspectral optical measurements were used to develop algorithms to estimate chlorophyll for the Great Salt Lake and New England lakes. The spectral data were used to mimic bands utilized by the MODIS, MERIS, and SeaWiFS sensors, as well as for a theoretical hyperspectral sensor with 3-nm wide bands, providing the capability to evaluate algorithm performance in all of these sensors. In addition to the traditional bands used in these algorithms, alternate band combinations were examined for both ocean color chlorophyll (OC) and maximum chlorophyll index (MCI) algorithms. A simulated 709 nm band was created for MODIS using the 754 nm band, providing a method for testing MODIS with algorithms relying on the key 705 nm to 715 nm wavelength range. In New England lakes, the most effective algorithm for hyperspectral bands (RMS = 0.206, in log decades) and MERIS (RMS = 0.218) was a version of MCI. For MODIS and SeaWiFS, the most effective algorithm used an OC approach with 489 nm as the blue band, yielding an RMS of 0.242 and 0.231, respectively. In the Great Salt Lake, the most effective algorithms for hyperspectral bands and MERIS were based on a single ratio of 709 nm / 675 nm, providing an RMS of 0.236 and 0.249, respectively. For MODIS and SeaWiFS, the most effective algorithm was the OC method using 489 nm as the blue band, which resulted in an RMS of 0.246 and 0.255, respectively