Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Pokrzywinski, Kaytee L.; Johansen, Richard; Reif, Molly; Bourne, Scott G.; Hammond, Shea; Fernando, Brianna M.

doi:10.1016/j.hal.2022.102268

Cited by 12 publications

(9 citation statements)

References 30 publications

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“…Pyo et al ( 2019) [33] used hyperspectral imagery data from airborne flight (ASIA Aero Survey) coupled with in situ measurements by a handheld spectroradiometer to estimate phycocyanin and Chl-a in a river case study. Pokrinwiskie et al (2022) [34] explores the synergy of hyperspectral UAV (Unmanned Aerial Vehicle), ground sampling and laboratory analyses to estimate Chl-a, turbidity, and phycocyanin in a pond-controlled environment.…”

Section: Quality Elements (Qes) and Water Body Typesmentioning

confidence: 99%

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

et al. 2023

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The development of a sustainable water quality monitoring system at national scale remains a big challenge until today, acting as a hindrance for the efficient implementation of the Water Framework Directive (WFD). This work provides valuable insights into the current state-of-the-art Earth Observation (EO) tools and services, proposing a synergistic use of innovative remote sensing technologies, in situ sensors, and databases, with the ultimate goal to support the European Member States in effective WFD implementation. The proposed approach is based on a recent research and scientific analysis for a six-year period (2017–2022) after reviewing 71 peer-reviewed articles in international journals coupled with the scientific results of 11 European-founded research projects related to EO and WFD. Special focus is placed on the EO data sources (spaceborne, in situ, etc.), the sensors in use, the observed water Quality Elements as well as on the computer science techniques (machine/deep learning, artificial intelligence, etc.). The combination of the different technologies can offer, among other things, low-cost monitoring, an increase in the monitored Quality Elements per water body, and a minimization of the percentage of water bodies with unknown ecological status.

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Section: Quality Elements (Qes) and Water Body Typesmentioning

confidence: 99%

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

et al. 2023

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“…Although there are no commercial "off-the-shelf" UAS platforms specifically designed for detecting cyanoHABs, the correct combination of platform and commercially available sensors can provide a unique opportunity to examine cyanobacteria and water quality at scales not possible with operational satellite imagers (Table 3). Additionally, UAS platforms can play a pivotal role in bridging the gap between traditional remote sensing and field-based monitoring (Castro et al 2020;Pokrzywinski et al 2022b).…”

Section: Airborne Remote Sensingmentioning

confidence: 99%

“…Recently, the use of advanced autonomous vehicles for water quality monitoring and modeling has been increasing. For example, improvements in UAS coupled with hyperspectral sensors have begun to advance water quality monitoring efforts, especially regarding algal species-level detection and estimation (Lyu et al 2016;Castro et al 2020;Pokrzywinski et al 2022b).…”

Section: Remote Sensing Modelsmentioning

confidence: 99%

A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs

Johansen¹,

Katzenmeyer²,

Pokrzywinski³

et al. 2023

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Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and compile approaches for water quality monitoring as well as statistical methodologies required to analyze and visualize highly diverse spatiotemporal water quality data. The literature review identified two broad categories: (1) sensor-based approaches for monitoring rapid response treatments of cyanobacterial harmful algal blooms (cyanoHABs), and (2) analytical tools and techniques to analyze complex high resolution spatial and temporal water quality data. The ultimate goal of this review is to provide the current state of the science as an array of scalable approaches, spanning from simple and practical to complex and comprehensive, and thus, equipping the US Army Corps of Engineers (USACE) water quality managers with options for technology-analysis combinations that best fit their needs.

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“…Additionally, there are numerous remote sensing algorithms for the detection of HABs, most with slight band or coefficient variations. Johansen et al (2022) [38] demonstrated that most empirically based remote sensing algorithms fell into only a few general algorithm formulas, including the following: Normalized Difference Chlorophyll Index (NDCI), Two-Band Difference Algorithm (2BDA), Three-Band Difference Algorithm (3BDA), and the Chlorophyll Index/Maximum Chlorophyll Index (CI/MCI). These empirical algorithms have been used for decades to detect water quality pigments and HABs across numerous satellite imagers, but their robustness and efficacy across space and time is understudied, limiting algorithm performance spatially and temporally (see reviews [19,38,39]).…”

Section: Introductionmentioning

confidence: 99%

A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a

Johansen,

Reif,

Saltus

et al. 2024

Sustainability

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Harmful algal blooms are a global phenomenon that degrade water quality and can result in adverse health impacts to both humans and wildlife. Monitoring algal blooms at scale is extremely difficult due to the lack of coincident data across space and time. Additionally, traditional field collection methods tend to be labor- and cost-prohibitive, resulting in disparate data collection not capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to help alleviate this issue by leveraging large, public, water quality databases coupled with open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental United States between 2019 and 2022. Data were aggregated via a field method and matched to coincident Sentinel-2 imagery using k-folds cross-validation to evaluate the performance of the band ratio algorithms at the nationwide scale. Additionally, the dataset was portioned to evaluate the influence of temporal windows and annual consistency on algorithm performance. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a, which performed moderately well (R2 > 0.5) across the entire continental united states, encompassing highly diverse spatial, temporal, and physical conditions. Algorithms’ performances were consistent across different field methods, temporal windows, and annually. The most compatible field data acquisition method was the chlorophyll a, water, trichromatic method, uncorrected with R2 values of 0.63, 0.62, and 0.41 and RMSE values of 15.89, 16.2, and 23.30 for 2BDA, NDCI, and MCI, respectively. These results indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is especially valuable when coincident data are unavailable or limited.

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Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Cited by 12 publications

References 30 publications

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs

A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a

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