Darryl J. Keith

Barriers to adopting satellite remote sensing for water quality management

Sustainable practices require a long-term commitment to creating solutions to environmental, social, and economic issues. The most direct way to ensure that management practices achieve sustainability is to monitor the environment. Remote sensing technology has the potential to accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long time periods, and is now proving useful in coastal waters, estuaries, lakes, and reservoirs, which are relevant to water quality managers. Comprehensive water quality climate data records have the potential to provide rapid water quality assessments, thus providing new and enhanced decision analysis methodologies and improved temporal/spatial diagnostics. To best realize the full application potential of these emerging technologies an open and effective dialogue is needed between scientists, policy makers, environmental managers, and stakeholders at the federal, state, and local levels. Results from an internal US Environmental Protection Agency qualitative survey were used to determine perceptions regarding the use of satellite remote sensing for monitoring water quality. The goal of the survey was to begin understanding why management decisions do not typically rely on satellite-derived water quality products.

Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean HICO sensor

The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring or existing ocean colour satellites. HICO, an Office of Naval Research-sponsored programme, is the first space-based maritime hyperspectral imaging instrument designed specifically for the coastal ocean. HICO has been operating since September 2009 from the Japanese Experiment Module – Exposed Facility on the International Space Station (ISS). The high pixel resolution (approximately 95 m at nadir) and hyperspectral imaging capability offer a unique opportunity for characterizing a wide range of water colour constituents that could be used to assess environmental condition. In this study, we transform atmospherically corrected ISS/HICO hyperspectral imagery and derive environmental response variables routinely used for evaluating the environmental condition of coastal ecosystem resources. Using atmospherically corrected HICO imagery and a comprehensive field validation programme, three regionally specific algorithms were developed to estimate basic water-quality properties traditionally measured by monitoring agencies. Results indicated that a three-band chlorophyll a algorithm performed best (R2 = 0.62) when compared with in situ measurement data collected 2–4 hours of HICO acquisitions. Coloured dissolved organic matter (CDOM) (R2 = 0.93) and turbidity (R2 = 0.67) were also highly correlated. The distributions of these water-quality indicators were mapped for four estuaries along the northwest coast of Florida from April 2010 to May 2012. However, before the HICO sensor can be transitioned from proof-of-concept to operational status and its data applied to benefit decisions made by coastal managers, problems with vicarious calibration of the sensor need to be resolved and standardized protocols are required for atmospheric correction. Ideally, the sensor should be placed on a polar orbiting platform for greater spatial and temporal coverage as well as for image synchronization with field validation efforts.

Performance evaluation of normalized difference chlorophyll index in northern Gulf of Mexico estuaries using the Hyperspectral Imager for the Coastal Ocean

The Hyperspectral Imager for the Coastal Ocean (HICO) was used to derive chlorophyll-a (chl-a) based on the normalized difference chlorophyll index (NDCI) in two Gulf of Mexico coastal estuaries. Chl-a data were acquired from discrete in situ water sample analysis and above-water hyperspectral surface acquisition system (HyperSAS) remote sensing reflectance in Pensacola Bay (PB) and Choctawhatchee Bay (CB). NDCI algorithm calibrations and validations were completed on HICO data. Linear and best-fit (polynomial) calibrations performed strongly with R2 of 0.90 and 0.96, respectively. The best validation of NDCI resulted with an R2 of 0.74 and root-mean-square error (RMSE) of 1.64 µg/L. A strong spatial correspondence was observed between NDCI and chl-a, with higher NDCI associated with higher chl-a and these areas were primarily located in the northern PB and eastern CB at the river mouths. NDCI could be effectively used as a qualitative chl-a monitoring tool with a reduced need for site-specific calibration.

Trophic status, ecological condition, and cyanobacteria risk of New England lakes and ponds based on aircraft remote sensing

Abstract. Aircraft remote sensing of freshwater ecosystems offers federal and state monitoring agencies an ability to meet their assessment requirements by rapidly acquiring information on ecosystem responses to environmental change for water bodies that are below the resolution of space-based platforms. During this study, hyperspectral data were collected over a two-day period from glacial lakes, ponds, and man-made reservoirs in New Hampshire, Massachusetts, Connecticut, and Rhode Island. These lakes ranged from five to greater than 1600 hectares and oligotrophic-mesotrophic to eutrophic and hypereutrophic conditions. Water samples were collected by several New England state agencies coincident with the airborne remote-sensing flights to provide ground reference data for algorithm development and testing. Using an inverse modeling approach remotely sensed reflectances from the near-infrared to red portion of the spectrum were used to develop an empirical model to estimate chlorophyll a concentrations. The accuracy of the algorithm was assessed from the RSM error of predicted and measured chlorophyll values for all lakes sampled. Results showed a strong statistical relationship between measured and predicted values. The predicted chlorophyll concentrations were used to assess the biological condition, trophic status, and recreational risk to human health for the New England lakes and ponds surveyed.

Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

Ocean color algorithms have been successfully developed to estimate chlorophyll a and total suspended solids concentrations in coastal and estuarine waters but few have been created to estimate light absorption due to colored dissolved inorganic matter (CDOM) and salinity from the spectral signatures of these waters. In this study, we used remotely sensed reflectances in the red and blue-green portions of the visible spectrum retrieved from Medium Resolution Imaging Spectrometer (MERIS) and the International Space Station (ISS) Hyperspectral Imager for the Coastal Ocean (HICO) images to create a model to estimate CDOM absorption. CDOM absorption results were then used to develop an algorithm to predict the surface salinities of coastal bays and estuaries in New England, Middle Atlantic, and Gulf of Mexico regions. Algorithm-derived CDOM absorptions and salinities were successfully validated using laboratory measured absorption values over magnitudes of ~0.1 to 7.0 m−1 and field collected CTD data from oligohaline to polyhaline (S less than 5 to 18–30) environments in Narragansett Bay (Rhode Island); the Neuse River Estuary (North Carolina); Pensacola Bay (Florida); Choctawhatchee Bay (Florida); St. Andrews Bay (Florida); St. Joseph Bay (Florida); and inner continental shelf waters of the Gulf of Mexico.

New Technology for Conducting Radiation Hazard Assessments: The Application of the Underwater Radiation Spectral Identification System (URSIS) at the Massachusetts Bay Industrial Waste (U.S.A.)

The Underwater Radiation Spectral Identification System (URSIS) is a portable spectrometer used for the in situ detection of radioactivity in the marine environment. This paper reports on the first time application of this technology to assess, in a preliminary manner, the potential radiation threat to the public and environment at an aquatic disposal site – the Massachusetts Bay Industrial Waste Site (IWS). Utilizing the meneuvering capabilities of ROV and manned submersible vehicles, the URSIS was successfully positioned close (5–10 cm) to waste containers for a period sufficient to detect, in real time, the presence of radioactive materials. Spectral data from 45 individual targets indicated that the radionuclides present in sediments which draped or partially buried waste containers were consistent with natural background concentrations. No man-made radionuclides were detected at any of the target or background measurement locations. These data support the conclusion that low-level radiation does not pose an imminent and widespread human health or ecological threat in Massachusetts Bay.

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

ABSTRACT In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R2 value of 0.66; root mean square error value of 8.9 µg l−1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.