Data Needs for Hyperspectral Detection of Algal Diversity Across the Globe

Dierssen, Heidi M.; Bracher, Astrid; Brando, Vittorio E.; Loisel, Hubert; Ruddick, Kevin

doi:10.5670/oceanog.2020.111

Cited by 30 publications

(21 citation statements)

References 17 publications

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“…As high-performance computing and easyto-implement parallel processing workflows have proliferated, increasing data volumes with increasing hyperspectral datasets and the associated challenges remain lock-step with computing advances. Data reduction is also common because of the limited amount of hyperspectral datasets coupled with biogeochemical information for algorithm development and validation (Dierssen et al, 2020). In addition, many approaches are not repeatable because the imagery has not been adequately corrected for atmospheric absorption and scattering and sea surface reflections.…”

Section: Looking Under the Hoodmentioning

confidence: 99%

“…To aid in algorithm development, we have compiled a list of hyperspectral datasets that may be useful for evaluating different approaches for estimating aquatic parameters, including phytoplankton and benthic community composition (Table 4). With more hyperspectral field data across a wide variety of conditions (Dierssen et al, 2020), particularly for applications for inland and coastal waters, we may find potentially new information in parts of the spectrum previously overlooked. Even without this further information, hyperspectral data will help reduce the uncertainty in the retrieved parameters (Werdell et al, 2018).…”

Section: Cooking Up An Algorithm Stormmentioning

confidence: 99%

“…As discussed above, data is often transformed first and then linked to a parameter of interest using linear or nonlinear regression techniques. This type of approach has been used for the current band ratio chlorophyll a algorithms and for other parameters like ancillary pigments, phytoplankton groups, and total suspended matter (Catlett and Siegel, 2018;Ortiz et al, 2019;Bracher et al, 2020;Smith and Bernard, 2020). For many aquatic parameters, the regression analysis is conducted on log-transformed data to cover the full range of environmental conditions.…”

Section: Parametric Regressionmentioning

confidence: 99%

“…Photosynthesizing flora includes different assemblages of phytoplankton which provide the bulk of the photosynthesis in the world ocean, and photosynthesizing fauna includes tropical coral reefs and select mixotrophic plankton like ciliates. Algorithms for estimating pigment concentration are usually associated with large uncertainty when applied globally because of the variability in size and shape of phytoplankton and cellular levels of pigment related to environmental conditions (Dierssen et al, 2020). Increasingly studies show that only a limited number of phytoplankton groups (∼5) may be differentiated globally using hyperspectral absorption spectra in visible wavelengths (Kramer and Siegel, 2019).…”

Section: Unlocking Biodiversity: Neither Fish Nor Fowlmentioning

confidence: 99%

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Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Dierssen

Ackleson

Joyce

et al. 2021

Front. Environ. Sci.

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Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities.

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Section: Looking Under the Hoodmentioning

confidence: 99%

Section: Cooking Up An Algorithm Stormmentioning

confidence: 99%

Section: Parametric Regressionmentioning

confidence: 99%

Section: Unlocking Biodiversity: Neither Fish Nor Fowlmentioning

confidence: 99%

See 2 more Smart Citations

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Dierssen

Ackleson

Joyce

et al. 2021

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…In some, especially remote areas affected by regularly occurring (harmful) algal blooms, monitoring strategies require increased observing capabilities (Sellner et al, 2003). Whilst detection and characterization of the spatial and temporal development of phytoplankton biomass is already feasible using existing multispectral Argo floats, the transformation toward hyperspectral technology will provide additional opportunities of tracking its composition and predict harmful algal bloom development (Shen et al, 2012;Dierssen et al, 2020). Ultimately, hyperspectral data based on Argo float may pave the way toward harmful algal bloom forecasting programs that require nearly real time observations from a global communication network (Jochens et al, 2010).…”

Section: Future Prospectsmentioning

confidence: 99%

Radiometry on Argo Floats: From the Multispectral State-of-the-Art on the Step to Hyperspectral Technology

Jemai¹,

Wollschläger²,

Voß³

2021

Front. Mar. Sci.

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Over the past two decades, robotic technology such as Argo floats have revolutionized operational autonomous measurement of the oceans. Recently, Biogeochemical Argo floats (BGC-Argo floats) have measured optical and biogeochemical quantities down to a depth of 2,000 m. Among these parameters, are measurements of the underwater light field from which apparent optical properties (AOPs), such as the diffuse attenuation coefficient for downwelling irradiance Kd(λ), can be derived. Presently, multispectral observations are available on this platform at three wavelengths (with 10–20 nm bandwidths) in the ultraviolet and visible part of the spectrum plus the Photosynthetically Available Radiation (PAR; integrated radiation between 400 and 700 nm). This article reviews studies dealing with these radiometric observations and presents the current state-of-the-art in Argo radiometry. It focus on the successful portability of radiometers onboard Argo float platforms and covers applications of the obtained data for bio-optical modeling and ocean color remote sensing. Generating already high-quality datasets in the existing configuration, the BGC-Argo program must now investigate the potential to incorporate hyperspectral technology. The possibility to acquire hyperspectral information and the subsequent development of new algorithms that exploit these data will open new opportunities for bio-optical long-term studies of global ocean processes, but also present new challenges to handle and process increased amounts of data.

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Drones address an observational blind spot for biological oceanography

Gray

Larsen

Johnston

2022

Frontiers in Ecol & Environ

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Marine biological communities are dynamic across many scales in both space and time. Such multi‐scale complexity complicates efforts to fully characterize these communities. Critical processes unfold on the order of 0.1–10 kilometers and 0.1–10 days, but conventional oceanographic techniques generally do not observe or model at this scale. Small aerial drones conveniently achieve scales of observation between satellite resolutions and in‐situ sampling, and effectively diminish the “blind spot” between these established measurement techniques. Despite this promise, drone‐based techniques face challenges inherent to optical oceanography, as well as logistical and regulatory barriers relating to both aerial and marine operations. Such obstacles have slowed adoption of drones for marine biological study, but best practices are emerging alongside new techniques that facilitate robust study designs and rigorous data collection. With such advancements, drones promise to complement conventional approaches in biological oceanography to more fully capture the spatiotemporal complexity of the marine environment.

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Data Needs for Hyperspectral Detection of Algal Diversity Across the Globe

Cited by 30 publications

References 17 publications

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Radiometry on Argo Floats: From the Multispectral State-of-the-Art on the Step to Hyperspectral Technology

Drones address an observational blind spot for biological oceanography

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