A5009348851- Marine and coastal ecosystems
- Water Quality Monitoring and Analysis
- Aquatic Ecosystems and Phytoplankton Dynamics
- Oil Spill Detection and Mitigation
- Fish Ecology and Management Studies
- Remote Sensing and LiDAR Applications
- Hydrology and Watershed Management Studies
- Marine Biology and Ecology Research
- Water Quality Monitoring Technologies
- Isotope Analysis in Ecology
- Marine Toxins and Detection Methods
- Remote Sensing and Land Use
- Metabolomics and Mass Spectrometry Studies
- Flood Risk Assessment and Management
- Underwater Vehicles and Communication Systems
- Coastal and Marine Management
- Automated Road and Building Extraction
- Water Quality and Pollution Assessment
- Geology and Paleoclimatology Research
- Geochemistry and Geologic Mapping
- Marine and fisheries research
- Oceanographic and Atmospheric Processes
- Remote Sensing in Agriculture
National Oceanic and Atmospheric Administration
2019-2024
NOAA Great Lakes Environmental Research Laboratory
2019-2024
Cherokee Nation
2019
Michigan United
2017
University of Minnesota, Duluth
2009
University of Minnesota System
2008
University of California, Santa Cruz
2006
Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, future missions is a long-standing challenge. Despite inherent differences sensors' spectral capability, spatial sampling, radiometric performance, research efforts focused on formulating, implementing, validating universal WQ algorithms continue to evolve. This extends recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs)...
Abstract The development of algorithms for remote sensing water quality (RSWQ) requires a large amount in situ data to account the bio-geo-optical diversity inland and coastal waters. GLObal Reflectance community dataset Imaging optical Aquatic environments (GLORIA) includes 7,572 curated hyperspectral reflectance measurements at 1 nm intervals within 350 900 wavelength range. In addition, least one co-located measurement chlorophyll , total suspended solids, absorption by dissolved...
Recurrent blooms of harmful algae and cyanobacteria (HABs) plague many coastal inland waters throughout the USA have significant socioeconomic impacts to adjacent communities. Notable HAB events in recent years continue underscore remaining gaps knowledge increased needs for technological advances leading early detection. This review summarizes main research management priorities that can be addressed through ocean observation-based approaches solutions algal blooms, provides an update state...
NOAA GLERL has routinely flown a hyperspectral imager to detect cyanobacteria harmful algal blooms (cyanoHABs) over the Great Lakes since 2015. Three consecutive years of imagery warn drinking water intake managers presence cyanoHABs. Western basin Lake Erie contributes weekly report Ohio Environmental Protection Agency using index (CI) as an indicator The CI is also used for NCCOS cyanoHAB bulletin applied satellite data. To date, there not been sensor comparison look at variability between...
Abstract Cyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these requires rapid detection CyanoHABs quantification toxins they produce. Here, we present a method using long‐range autonomous underwater vehicle (LRAUV) equipped with 3 rd ‐generation Environmental Sample Processor (3G‐ESP) search for adaptively sample areas high chlorophyll potentially representative CyanoHAB biomass....
Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These can be detected using optical radiometers due the presence of phycocyanin (PC) pigments. The spectral resolution best-available multispectral sensors limits their ability diagnostically detect PC in other photosynthetic To assess role determination PC, a large ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N...
An accurate inventory of unpaved road network length and condition within a county, state, or region is important for efficient use resources to manage maintain this critical transportation asset. Object-based classification techniques provide cost-effective way identify roads local agency's when the type (i.e., paved versus unpaved) attribute missing. We present Trimble eCognition® algorithm using four band optical aerial imagery object-based classify as unpaved. The ruleset evaluates...
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