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Richard P. Stumpf

Publications and source records attributed to Richard P. Stumpf.

17 recordsLinked to original sources
USGS2026

Cyanobacteria Assessment Network: Pilot study with Sentinel-2 derived chlorophyll data

Harmful algal blooms (HABs) affect global water quality, limiting uses like recreation and consumption because of excessive algal biomass and toxin production. These blooms cause surface scums, taste and odor issues, hypoxia, and negative health and socioeconomic impacts. The US Army Corps of Engineers (USACE) manages over 400 lakes and reservoirs and seeks to improve monitoring of HABs through Sentinel-2 (S2) satellite imagery. The purpose of this project is to develop workflows for a national chlorophyll-a product with 20 m spatial resolution, enabling HAB monitoring for over 270,000 lakes and reservoirs. The 1-year pilot study established a federal partnership to conduct preliminary S2 processing steps, using datasets from Florida, Ohio, and Oregon. Key steps included in situ data aggregation, data assimilation, S2 algorithm evaluation, spatial and temporal compositing, satellite sensor cross-validation, and web hosting of an example prototype product. The study covers 29 USACE reservoirs, with cross-validation using Sentinel-3 (S3) data from 212 lakes. The multiagency initiative aligns with federal agency missions to protect health and the environment, supports acts like the 2017 Harmful Algal Bloom and Hypoxia Research and Control Act, and has annual potential avoided costs of $42 million.

contiguous United States↗
USGS2021

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria . Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

Ecological Indicators↗
USGS2021

Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data

Widespread occurrence of cyanobacterial harmful algal blooms (CyanoHABs) and the associated health effects from potential cyanotoxin exposure has led to a need for systematic and frequent screening and monitoring of lakes that are used as recreational and drinking water sources. Remote sensing-based methods are often used for synoptic and frequent monitoring of CyanoHABs. In this study, one such algorithm – a sub-component of the Cyanobacteria Index called the CI cyano , was validated for effectiveness in identifying lakes with toxin-producing blooms in 11 states across the contiguous United States over 11 bloom seasons (2005–2011, 2016–2019). A matchup data set was created using satellite data from MEdium Resolution Imaging Spectrometer (MERIS) and Ocean Land Colour Imager (OLCI), and nearshore, field-measured Microcystins (MCs) data as a proxy of CyanoHAB presence. While the satellite sensors cannot detect toxins, MCs are used as the indicator of health risk, and as a confirmation of cyanoHAB presence. MCs are also the most common laboratory measurement made by managers during CyanoHABs. Algorithm performance was evaluated by its ability to detect CyanoHAB ‘Presence’ or ‘Absence’, where the bloom is confirmed by the presence of the MCs. With same-day matchups, the overall accuracy of CyanoHAB detection was found to be 84% with precision and recall of 87 and 90% for bloom detection. Overall accuracy was expected to be between 77% and 87% (95% confidence) based on a bootstrapping simulation. These findings demonstrate that CI cyano has utility for synoptic and routine monitoring of potentially toxic cyanoHABs in lakes across the United States.

Science for the Total Environment↗
USGS2019

Measurement of cyanobacteria bloom magnitude using satellite remote sensing

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Florida, Ohio↗
USGS2018

Performance metrics for the assessment of satellite data products: An ocean color case study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r 2 ), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.

Optics Express↗
USGS2017

Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources

Cyanobacterial harmful algal blooms (cyanoHAB) cause extensive problems in lakes worldwide, including human and ecological health risks, anoxia and fish kills, and taste and odor problems. CyanoHABs are a particular concern in both recreational waters and drinking water sources because of their dense biomass and the risk of exposure to toxins. Successful cyanoHAB assessment using satellites may provide an indicator for human and ecological health protection. In this study, methods were developed to assess the utility of satellite technology for detecting cyanoHAB frequency of occurrence at locations of potential management interest. The European Space Agency's MEdium Resolution Imaging Spectrometer (MERIS) was evaluated to prepare for the equivalent series of Sentinel-3 Ocean and Land Colour Imagers (OLCI) launched in 2016 as part of the Copernicus program. Based on the 2012 National Lakes Assessment site evaluation guidelines and National Hydrography Dataset, the continental United States contains 275,897 lakes and reservoirs >1 ha in area. Results from this study show that 5.6% of waterbodies were resolvable by satellites with 300 m single-pixel resolution and 0.7% of waterbodies were resolvable when a three by three pixel (3 × 3-pixel) array was applied based on minimum Euclidian distance from shore. Satellite data were spatially joined to U.S. public water surface intake (PWSI) locations, where single-pixel resolution resolved 57% of the PWSI locations and a 3 × 3-pixel array resolved 33% of the PWSI locations. Recreational and drinking water sources in Florida and Ohio were ranked from 2008 through 2011 by cyanoHAB frequency above the World Health Organization’s (WHO) high threshold for risk of 100,000 cells mL −1 . The ranking identified waterbodies with values above the WHO high threshold, where Lake Apopka, FL (99.1%) and Grand Lake St. Marys, OH (83%) had the highest observed bloom frequencies per region. The method presented here may indicate locations with high exposure to cyanoHABs and therefore can be used to assist in prioritizing management resources and actions for recreational and drinking water sources.

Florida, Ohio↗
USGS2017

A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing

Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remote sensing provides a potential technology for identifying cyanoHABs in multiple water bodies and across geo-political boundaries. An assessment method was developed using MEdium Resolution Imaging Spectrometer (MERIS) imagery to quantify cyanoHAB surface area extent, transferable to different spatial areas, in Florida, Ohio, and California for the test period of 2008 to 2012. Temporal assessment was used to evaluate changes in satellite resolvable inland waterbodies for each state of interest. To further assess cyanoHAB risk within the states, the World Health Organization’s (WHO) recreational guidance level thresholds were used to categorize surface area of cyanoHABs into three risk categories: low, moderate, and high-risk bloom area. Results showed that in Florida, the area of cyanoHABs increased largely due to observed increases in high-risk bloom area. California exhibited a slight decrease in cyanoHAB extent, primarily attributed to decreases in Northern California. In Ohio (excluding Lake Erie), little change in cyanoHAB surface area was observed. This study uses satellite remote sensing to quantify changes in inland cyanoHAB surface area across numerous water bodies within an entire state. The temporal assessment method developed here will be relevant into the future as it is transferable to the Ocean Land Colour Instrument (OLCI) on Sentinel-3A/3B missions.

California, Florida, Ohio↗
USGS2015

Agencies collaborate, develop a cyanobacteria assessment network

Cyanobacteria are a genetically diverse group of photosynthetic microorganisms that occupy a broad range of habitats on land and water all over the world. They release toxins that can cause lung and skin irritation, alter the taste and odor of potable water, and cause human and animal illness. Cyanobacteria blooms occur worldwide, and climate change may increase the frequency, duration, and extent of these bloom events. Rapid detection of potentially harmful blooms is essential to protect humans and animals from exposure. Information about potential for exposure, such as bloom duration, frequency, and extent, is especially critical for developing environmental management decisions during periods of limited resources and funding. The National Research Council (NRC) report Exposure Science in the 21st Century suggested that effectively assessing and mitigating exposures requires techniques for rapid measurement of a stressor, such as an algal bloom, across diverse geographic, temporal, and biologic scales (e.g., various bloom concentrations) and an enhanced infrastructure to address threats [ NRC , 2012]. The report specifically calls for approaches that use diverse information, such as satellite remote sensing, to identify and understand exposures that may pose a threat to ecosystems or human health. A collaborative effort integrates the work of the U.S. Environmental Protection Agency (EPA), NASA, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Geological Survey (USGS) to provide an approach for using satellite ocean color capabilities in U.S. fresh and brackish water quality management decisions. The overarching goal of this collaborative project is to detect and quantify cyanobacteria blooms using satellite data records in order to support the environmental management and public use of U.S. lakes and reservoirs. Satellite remote sensing tools may enable policy makers and environmental managers to assess the sustainability of watershed ecosystems and the services they provide, now and in the future. Satellite technology allows us to develop early-warning indicators of cyanobacteria blooms at the local scale while maintaining continuous national coverage.

Eos, Earth and Space Science News↗
USGS2012

Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms

The new satellite ocean color sensors offer a means of detecting and monitoring algal blooms in the ocean and coastal zone. Beginning with SeaWiFS (Sea Wide Field-of-view Sensor) in September 1997, these sensors provide coverage every 1 to 2 days with 1-km pixel view at nadir. Atmospheric correction algorithms designed for the coastal zone combined with regional chlorophyll algorithms can provide good and reproducible estimates of chlorophyll, providing the means of monitoring various algal blooms. Harmful algal blooms (HABs) caused by Karenia brevis in the Gulf of Mexico are particularly amenable to remote observation. The Gulf of Mexico has relatively clear water and K. brevis , in bloom conditions, tends to produce a major portion of the phytoplankton biomass. A monitoring program has begun in the Gulf of Mexico that integrates field data from state monitoring programs with satellite imagery, providing an improved capability for the monitoring of K. brevis blooms.

Gulf of Mexico↗
USGS2004

Historic topographic sheets to satellite imagery—A methodology for evaluating coastal change in Florida's Big Bend tidal marsh

This open-file report details the methodology used to rectify, digitize, and mosaic nineteen 19th century topographic sheets on the marsh-dominated Big Bend Gulf coast of Florida. Historic charts of tidal marshes in Florida's Big Bend were prepared in a digital grid-based format for comparison with modern features derived from 1995 satellite imagery. The chart-by-chart rectification process produced a map accuracy of ± 8 m. An effort was made to evaluate secondary map features, such as tree islands, but changes during the intervening years exceed standard surveying errors and rendered the analysis ineffective. A map, at 1:300,000 comparing historic and modern features, is provided to illustrate major changes along the coastline. Shoreline erosion is exceeded by the inland migration of the intertidal zone onto adjoining coastal forest lands. While statements of mapping accuracy are provided in the text, graphic representation of changes in the intertidal zone may be inexact at any given location. Thus caution is advised for site-specific applications. Maps and digital files provided should be used to visualize overall trends and regional anomalies, and not used to critically assess features at a particular location. Final product includes mosaic of historic coastal features and comparison to modern features.

Florida↗
USGS2003

Determination of water depth with high-resolution satellite imagery over variable bottom types

A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms--the standard linear transform and the new ratio transform--were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10-15 m. However, the linear transform does not distinguish depths >15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in >25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4-5 pixels) in water depths >15-20 m. In general, the ratio transform is more robust than the linear transform.

Limnology and Oceanography↗
USGS1999

Sea-level rise and coastal forests on the Gulf of Mexico

In this report, we review literature pertaining to the response of coastal forests on the Gulf of Mexico to sea-level rise. The report is divided into chapters that discuss effects of sea-level rise on non-mangrove forests, effects of sea-level rise on mangrove forests, and responses of Gulf coast trees to increased flooding and salinity. An assessment of the extent of coastal forest likely to be affected by sea-level rise in the near future is beyond the scope of this report. Chapter 1 discusses sea-level effects on non-mangrove forests of the Gulf of Mexico including descriptions of how coastlines and, therefore, coastal forests have changed through the Holo~ene, a discussion of how coastal geomorphology and hydrology may affect interactions of coastal forest with sea-level rise, and an evaluation of the role that hurricanes might play in coastal forest retreat. Literature on geological changes throughout the Holocene is not fully reviewed; sufficient information is given to place current rates of coastal forest retreat into historical perspective. Similarly, effects of hurricanes on coastal forests are not fully reviewed. Information that is pertinent to the role of hurricanes in coastal forest retreat and to the effects of hurricanes on the forest's interaction with sea level is presented. For more extensive reviews of hurricane effects on coastal forests of the Caribbean and the southeastern United States, the reader is referred to issues of BioScience (1994, Vol 44, no. 4), Biotropica (1991, "Special Issue: Ecosystem, Plant, and Animal Responses to Hurricanes in the Caribbean", Vol. 23, No. 4, Part A) and the Journal of Coastal Research (1991, "Impacts of Hurricane Hugo, Special Issue No. 8; and 1995, "Imprcts of Hurricane Andrew on the Coastal Zones of Florida and Louisiana: 22-26 August 1992", Special Issue No. 21). Selected pertinent information from Virginia, South and North Carolina has been included in this review, because much of our knowledge of coastal forest response to hurricanes, salinity and flooding comes from the Atlantic coast. These areas are floristically similar to the northern Gulf of Mexico. Species distribution of coastal forest trees and shrubs on the Gulf of Mexico are listed in Appendix 1. Extensive research has been conducted on the responses of individual tree species to increased salinity and flooding. Synthesis of this research, although very important for evaluating effects of sea-level rise and various hydrological modifications on coastal forest, is beyond the scope of this review. These studies are included as an annotated bibliography (Appendix 2). Selected field studies on tree responses to changes in hydrology and salinity (impoundments, drainage, etc.) are referenced in Appendix 3. A review of mangrove response to sea-level rise is included in Chapter 2. Mangrove forests are discussed in the context of Holocene changes in geomorphology. Predictions of the fate of mangroves in the face of future sea-level rise, as well as a review of factors that affect interactions between mangroves and sea-level rise are discussed. Selected references from outside the United States are included, because much of our knowledge of how mangroves react to sea-level rise comes from other tropical areas of the world. A discussion of hurricane effects on the mangroves of south Florida highlights gaps in our knowledge of how hurricanes affect responses of mangrove vegetation to sea-level rise there. Mangrove responses to temperature and elevated CO2 are briefly mentioned. These factors, as well as sea level, are expected to change in the future and may affect mangrove distribution.

Open-File Report↗
USGS1998

Satellite imagery of the north-central Gulf of Mexico, June 1993-May 1997

This CD contains imagery collected by the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA polar-orbiting weather satellites. The AVHRR provides almost daily coverage of a site at a resolution of approximately 1 km. The data types included are sea surface temperature (SST), water reflectance (REF), and a false-color infrared overview (FCI).

Alabama, Florida, Louisiana, Mississippi↗
USGS1997

Assessment of acreage and vegetation change in Florida's Big Bend tidal wetlands using satellite imagery

Fluctuations in sea level and impending development on the west coast of Florida have aroused concern for the relatively pristine tidal marshes of the Big Bend. Landsat Thematic Mapper (TM) images for 1986 and 1995 are processed and evaluated for signs of change. The images cover 250 km of Florida's Big Bend Gulf Coast, encompassing 160,000 acres of tidal marshes. Change is detected using the normalized difference vegetation index (NDVI) and land cover classification. The imagery shows negligible net loss or gain in the marsh over the 9-year period. However, regional changes in biomass are apparent and are due to natural disturbances such as low winter temperatures, fire, storm surge, and the conversion of forest to march. Within the marsh, the most prominent changes in NDVI and in land cover result from the recovery of mangroves from freezes, a decline of transitional upland vegetation, and susceptibility of the marsh edge and interior to variations in tidal flooding.

Florida↗
USGS1991

Observation of suspended sediments in Mobile Bay, Alabama from satellite

As part of a comprehensive geologic study of coastal Alabama and Mississippi, the U.S. Geological Survey is investigating coastal sediment transport in Mobile Bay and the adjacent shelf. Satellite imagery from the NOAA AVHRR is being used to provide data on the variability of spatial patterns in the near-surface suspended sediment concentration. This imagery is processed using atmospheric corrections to remove haze and Rayleigh radiance in order to obtain water reflectances; the reflectances are than converted to approximate sediment concentrations using standard relationships between water reflectance and in situ sediment concentrations. A series of images from early 1990 shows rapid changes in sediment concentrations in response to high river flow of the Alabama-Tombigbee river system. During these times, suspended sediment tends to flow out Mobile Bay without mixing into the eastern lobe of the Bay (Bon Secour Bay). The sediment concentration field also appears to be disturbed by the main ship channel. The sediment plume extends more than 60 km offshore after the peak flow event. One wind event in December 1989 was identified as increasing sediment concentration in the Bay. It is not believed that such an event has been previously observed from satellite.

Conference Paper↗