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16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Bloom Modeling and Prediction of the Harmful Algae Alexandrium in Bellingham Bay, WA

Bellingham, Washington is located near the American-Canadian border in the northwestern part of Washington state, and is bordered on its west side by the Lummi reservation. Between Lummi and Bellingham lies Bellingham bay, which has a history of harmful algae related closures dating back to 1978. The subject of this work is a genus of dinoflagellates: Alexandrium, within which many species have the capacity to produce a suite of toxins known as saxitoxin. These toxins bioaccumulate in bivalves, which in turn cause paralytic shellfish poisoning in marine consumers (mammals, birds, and fish), including humans. Symptoms in humans can range from tingling and numbness to difficulty or inability to breathe, resulting in death. Because of the longstanding history of shellfish gathering among Salish tribes and the fact that Washington is the leading U.S. producer of farmed bivalves, harmful algae blooms impact both native and non-native peoples living in the Salish Sea area negatively. The objective of this work is to identify factors which influence Alexandrium blooms in Bellingham Bay, as well as predict Alexandrium blooms in the future. The method of doing so involves two processes: an initial statistical modeling phase to find in situ and remote sensing observations correlated to bloom density (including, but not limited to: water temperature, chlorophyll-a, salinity, color dissolved organic matter, and discharge rate of local rivers), followed by use of that data as a training set for a recursive neural network. This predictive capacity may inform future closures, help ensure the safety of shellfish consumers, and act as a baseline for future modeling efforts in the region.

Harmful↗
NASA NTRS2022

Evaluation of Retrieving Chlorophyll a Concentration and Colored Dissolved Organic Matter Absorption from Satellite Ocean Color Remote Sensing in the Coastal Waters of Hokkaido, Japan

Coastal ecosystems are important in biogeochemical cycles, because they provide many pathways for chemical elements and compounds to flow between the physical environment and living organisms. The spatial and temporal scales of the biotic and abiotic interactions establish the inherent dynamics of the coastal zone and the requirement for high-resolution investigations. Using match-up data collected in Hokkaido coastal waters, the study herein evaluated the performance of deriving chlorophyll (Chl) a concentration and colored dissolved organic matter (CDOM) absorption (a(sub CDOM)) as a function of wavelength (λ) from satellite observations. The Japanese Aerospace Exploration Agency (JAXA) Second generation GLobal Imager (SGLI), which has 250 m spatial resolution, was compared with other satellite ocean color (OC) sensors. Our results show that the standard SGLI OC4 algorithm with the 530 nm band provided the best performance for Chl a retrievals, and the end-member analysis (EMA) technique improved the estimation of a(sub CDOM)(λ) . Overall differences between in situ radiometric data and satellite retrievals suggest that additional challenges remain, especially in the ultraviolet and blue spectral domains which are useful for studying CDOM and harmful algae blooms in coastal waters. To fulfill applications wherein high-quality remote sensing data are required, the improvement of the atmospheric correction is a likely research area where additional accomplishment will be beneficial for satellite observations of optically complex coastal waters.

SGLI/GCOM-C↗
NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

Zachary Fasnacht↗
NASA NTRSDate not supplied

Principal Component and Machine Learning Approach to Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals can be limited spatially due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals. Despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we developed a spatial gap filling approach applying machine learning approach to hyperspectral instruments to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral components that describe the scattering and absorption of the atmosphere mixed with the surface spectral signatures. The coefficients of the principal components are used to train a neural network to predict ocean color properties derived from a standard MODIS ocean color algorithm. We apply the approach to two hyperspectral UV/VIS sensors, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) onboard upcoming NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the first NASA and Smithsonian geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) spectrometer to better understand diurnal variability in inland and coastal ocean ecology.

Zachary Fasnacht↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRSDate not supplied

Life After Launch: A Snapshot of the First 6 Months of NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission launched from Kennedy Space Center in the early morning of February 8, 2024. Just 63 days later, data from NASA’s newest Earth-observing satellite became available to the public. These data will extend and improve upon NASA’s 20+ years of global satellite observation of our living oceans, atmospheric aerosols, and cloud and initiate an advanced set of climate-relevant data records. Ultimately, PACE is the first mission to provide daily, global measurements that will enable prediction of the “boom-bust” cycle of fisheries, the appearance of harmful algae, and other factors that affect commercial and recreational industries. PACE also observes clouds and tiny airborne particles known as aerosols that influence air quality and absorb and reflect sunlight, thus warming and cooling the atmosphere. In the months since launch and initial data release, the PACE Project pursued instrument temporal and system vicarious calibrations, executed cross-instrument comparisons, conducted performance assessments, explored synergies with other missions, and released advanced science data products. In parallel, the PACE Validation Science Team left for the field and the Post-launch Airborne eXperiment (PACE-PAX) prepared for its mission. And, most importantly, preliminary science results were realized. Here, we present a snapshot of these activities and their impacts and outcomes, encompassing the first half year of the PACE mission.

PACE↗
NASA NTRS2018

Satellite Remote Sensing: Ocean Color

Satellite ocean color instruments routinely provide global, synoptic views of the Earth's marine biosphere. These spaceborne radiometers measure light exiting the top of the atmosphere at discrete wavelengths in the ultraviolet to shortwave infrared region of the spectrum. This includes measurements of the color of the ocean - information used to infer the contents of the sunlit upper ocean, such as concentrations of phytoplankton, suspended sediments, and dissolved organic carbon. Continuous marine biological, ecological, and biogeochemical data records from satellite ocean color instruments now span over twenty years. This time-series not only supports Earth system and climate research, but also ecosystem and watershed management activities, including detection of nuisance and harmful algal blooms.

Biosphere↗
NASA NTRS2022

Satellite-derived Cyanobacteria Frequency and Magnitude in Headwaters & Near-dam Reservoir Surface Waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Amber R Ignatius↗
NASA NTRS2022

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

cyanobacteria↗
NASA NTRSDate not supplied

Identifying Indicators of Harmful Algal Blooms in Coastal Atacama Using Satellite Image Processing Techniques to Improve Industry and Authority Response

Over the past decade, Chile has experienced a significant rise in both the frequency and intensity of coastal Harmful Algal Blooms (HABs). These bloom events are a growing concern for the Atacama Region especially, with potential impacts on human health, aquaculture, and the environment. HABs are caused by an excess proliferation of microalgae, with certain algae species commonly found in Chilean HABs capable of producing toxins which can poison fish, toxify shellfish, and cause illness or death when ingested by humans. Collaborating with the Ministry of Health of Chile, Centro de Información de Recursos Naturales, the University of Atacama, and the Embassy of Chile’s Agricultural Office, this study identifies potential indicators of HABs in the Coastal Atacama Region using NASA Earth observations. Satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA’s Aqua satellite was used for the identification of chlorophyll-a which provides direct estimates of the algae pigment’s concentration in water bodies. In addition, the spatiotemporal patterns of several other parameters were examined to identify correlations with occurrences of HABs, including sea surface temperature and normalized fluorescence line height using Aqua MODIS, Normalized Difference Turbidity Index (NDTI) using Landsat 8, and ocean wind speed measurements from the NOAA Joint Polar Satellite System. Analysis of these patterns revealed hotspots of high chlorophyll-a concentrations from the years 2014 to 2022, the most major of which occurring in the coastal zones of Chañaral, Bahía Inglesa, and Huasco. Also, through the use of a similarity search conducted, the seasonal variation of these indicators was mapped to highlight areas with great likelihood of experiencing HABs in the future. The results can guide future efforts for in-situ water sampling, toxin analysis and assessment, and HAB monitoring and prevention.

Cody O'Ferrall↗
NASA NTRS2007

Great Lakes Hyperspectral Water Quality Instrument Suite for Airborne Monitoring of Algal Blooms

NASA Glenn Research Center and NOAA Great Lakes Environmental Research Lab are collaborating to utilize an airborne hyperspectral imaging sensor suite to monitor Harmful Algal Blooms (HABs) in the western basin of Lake Erie. The HABs are very dynamic events as they form, spread and then disappear within a 4 to 8 week time period in late summer. They are a concern for human health, fish and wildlife because they can contain blue green toxic algae. Because of this toxicity there is a need for the blooms to be continually monitored. This situation is well suited for aircraft based monitoring because the blooms are a very dynamic event and they can spread over a large area. High resolution satellite data is not suitable by itself because it will not give the temporal resolution due to the infrequent overpasses of the quickly changing blooms. A custom designed hyperspectral imager and a point spectrometer mounted on aT 34 aircraft have been used to obtain data on an algal bloom that formed in the western basin of Lake Erie during September 2006. The sensor suite and operations will be described and preliminary hyperspectral data of this event will be presented

Lekki, John↗
NASA NTRS2019

Applicability of Spectral Decomposition by Varimax-Rotated, Principal Component Analysis to the Surface Biology and Geology (SBG) VNIR Mission Concept

Cyanobacterial and Harmful Algal Blooms (CyanoHABs) are a growing concern in coastal and inland waters. But, spectral interference from multiple constituents in optically complex waters can hamper application of remote sensing using traditional image processing methods. The Kent State University (KSU) spectral decomposition method can be applied to multispectral and hyperspectral remote sensing images (e.g. HICO and the NASA Glenn HSI2) to partition and identify signals related to cyanobacteria, algae, pigment degradation products and suspended sediment in each pixel. Fundamental to the use of remote sensing data is the ability to extract independent signals from correlated hyperspectral VNIR data cubes. The Kent State University varimax-rotated, principal component analysis method (VPCA) is important to integrate into the SBG VNIR mission concept because it provides greater specificity, a software-based SNR boost relative to hardware performance, and can assist with Cal/Val, Modeling and Applications. We present examples of the hyperspectral application of the KSU VPCA method with relevance to SBG. The information extracted by VPCA can be validated spectrally or spatially with laboratory and/or in situ sensors, which capture spatial or time series of information at discrete points within remote sensing images. Comparisons show hyperspectral sensors extract more components than multispectral ones, but more independent information can be extracted from multispectral sensors by VPCA than traditional band ratio approaches. The spectral decomposition method is capable of enhancing the signal to noise ratio (SNR) of the NASA Glenn, second-generation hyperspectral imager by a factor of 7x to 20x, with a spectral reproducibility of ±3%. The spectral decomposition method, when compared against existing remote sensing monitoring methods exhibits both greater specificity and a lower detection limit. The method has been validated with multispectral images in Lake Erie to quantify the Microcystis CyanoHAB and from the Indian River Lagoon, Florida to quantify the Brown Tide resulting from A. lagunesnsis. Field operations in the Western Basin of Lake Erie were conducted using a bbe Fluoroprobe to collect vertical profiles and horizontal tows along a transect from the Toledo to the Detroit Lighthouse during coincident satellite overpasses. Extraction of pixel values from the MODIS Aqua sensor yields agreement between in situ field and lab-based measures of cyanobacterial, cryptophyte, diatoms and green algae, suspended sediment and pigment degradation products with R2>0.8.

hyperspectral↗
NASA NTRS2002

Clues to Coral Reef Health: Integrating Radiative Transfer Modeling and Hyperspectral Data

An important contribution to coral reef research is to improve spectral distinction between various health states of coral species in areas subject to harmful anthropogenic activity and climate change. New insights into radiative transfer properties of corals under healthy and stressed conditions can advance understandings of ecological processes on reefs and allow better assessments of the impacts of large-scale bleaching and disease events, Our objective was to examine the spectral and spatial properties of hyperspectral sensors that may be used to remotely sense changes in reef community health. We compare in situ reef environment spectra (healthy coral, stressed coral, dead coral, algae, and sand) with airborne hyperspectral data to identify important spectral characteristics and indices. Additionally, spectral measurements over a range of water depths, relief, and bottom types are compared to help quantify bottom-water column influences. In situ spectra were collected in July and August 2002 at the Long Rock site in the Andros Island, Bahamas coastal zone coral reef. Our primary emphasis was on Acropora palmata (or elkhorn coral), a major reef building coral, which is prevalent in the study area, but is suffering from white band disease. A. palmata is currently being, proposed as an endangered species because its populations have severely declined in many areas of the Caribbean. In addition to the A. palmata biotope, we have collected spectra of at least seven other coral biotopes that exist within the study area, each with different coral community composition, density of corals, relief, and size of corals. Coral spectral reflectance was then input into a radiative transfer model, CORALMOD (CM1), which is based on a leaf radiative transfer model. In CM1, input coral reflectance measurements produce modeled reflectance through an inversion at each visible wavelength to provide the absorption spectrum. Initially, we imposed a scattering baseline that is the same regardless of the coral condition and that coral is optically thick and no light is transmitted through coral. Here we will focus on methodology, experimental design, and initial findings of the in situ spectral measurements and preliminary output from the radiative transfer model.

Guild, Liane↗
NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗