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All Funded Projects

Filter Total Items: 170

Modernizing sensor data workflows to leverage Internet of Things (IoT) and cloud-based technologies

Drought is a major problem in the American Southwest that is expected to worsen under the effects of climate change. Currently, the Southwest Biological Science Center is monitoring the effects of drought with soil moisture probes in a range of ecosystems across an elevational gradient on the Colorado Plateau. These data are used in multiple studies to analyze the effects of drought on...
Modernizing sensor data workflows to leverage Internet of Things (IoT) and cloud-based technologies

Modernizing sensor data workflows to leverage Internet of Things (IoT) and cloud-based technologies

Drought is a major problem in the American Southwest that is expected to worsen under the effects of climate change. Currently, the Southwest Biological Science Center is monitoring the effects of drought with soil moisture probes in a range of ecosystems across an elevational gradient on the Colorado Plateau. These data are used in multiple studies to analyze the effects of drought on vegetation
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Making USGS/NOAA Total Water Level and Coastal Change Forecast data accessible through user-friendly interfaces

The Total Water Level and Coastal Change Forecast delivers 6-day forecasts of hourly water levels and the probability of waves impacting dunes along 5000 km of sandy coasts along the Atlantic and Gulf of Mexico and will soon expand to the Pacific. These forecasts provide needed information to local governments and federal partners and are used by the USGS to place sensors before a storm...
Making USGS/NOAA Total Water Level and Coastal Change Forecast data accessible through user-friendly interfaces

Making USGS/NOAA Total Water Level and Coastal Change Forecast data accessible through user-friendly interfaces

The Total Water Level and Coastal Change Forecast delivers 6-day forecasts of hourly water levels and the probability of waves impacting dunes along 5000 km of sandy coasts along the Atlantic and Gulf of Mexico and will soon expand to the Pacific. These forecasts provide needed information to local governments and federal partners and are used by the USGS to place sensors before a storm. The
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From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making

The USGS maintains an extensive monitoring network throughout the United States in order to protect the public and help manage natural resources. This network generates millions of data points each year, all of which must be evaluated and reviewed manually for quality assurance and control. Sensor malfunctions and issues can result in data losses and unexpected costs, and are typically...
From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making

From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making

The USGS maintains an extensive monitoring network throughout the United States in order to protect the public and help manage natural resources. This network generates millions of data points each year, all of which must be evaluated and reviewed manually for quality assurance and control. Sensor malfunctions and issues can result in data losses and unexpected costs, and are typically only
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Processing a new generation of hyperspectral data on the Cloud using Pangeo

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise...
Processing a new generation of hyperspectral data on the Cloud using Pangeo

Processing a new generation of hyperspectral data on the Cloud using Pangeo

We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary
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So you want to build a decision support tool? Assessing successes, pitfalls, and lessons learned for tool design and development

The purpose of this study is to understand how the USGS is using decision support, learning from successes and pitfalls in order to help streamline the design and development process across all levels of USGS scientific tool creation and outreach. What should researchers consider before diving into tool design and development? Our goal is to provide a synthesis of lessons learned and...
So you want to build a decision support tool? Assessing successes, pitfalls, and lessons learned for tool design and development

So you want to build a decision support tool? Assessing successes, pitfalls, and lessons learned for tool design and development

The purpose of this study is to understand how the USGS is using decision support, learning from successes and pitfalls in order to help streamline the design and development process across all levels of USGS scientific tool creation and outreach. What should researchers consider before diving into tool design and development? Our goal is to provide a synthesis of lessons learned and best
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Development of a Flexible Multi-Channel Spatiotemporal Geophysical HDF5 Data Format Supporting FAIR

A unique opportunity for USGS to collaborate with IRIS-PASSCAL (the national seismic instrument facility) has presented itself to develop a geophysical data archive format that follows FAIR principles. IRIS-PASSCAL is extending facility to include magnetotelluric (MT) instruments prescribing the need for them to archive collected MT data by extending their existing protocol. Concurrently
Development of a Flexible Multi-Channel Spatiotemporal Geophysical HDF5 Data Format Supporting FAIR

Development of a Flexible Multi-Channel Spatiotemporal Geophysical HDF5 Data Format Supporting FAIR

A unique opportunity for USGS to collaborate with IRIS-PASSCAL (the national seismic instrument facility) has presented itself to develop a geophysical data archive format that follows FAIR principles. IRIS-PASSCAL is extending facility to include magnetotelluric (MT) instruments prescribing the need for them to archive collected MT data by extending their existing protocol. Concurrently
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Real-time Coastal Salinity Index for monitoring coastal drought and ecological response to changing salinity values

Many coastal areas are experiencing departures from normal conditions due to changing land use and climate patterns, including increased frequency, severity, or duration of floods and droughts, in some cases combinations of the two. To address these issues, the U.S. Geological Survey developed the Coastal Salinity Index (CSI) to identify and communicate fluctuating salinity conditions...
Real-time Coastal Salinity Index for monitoring coastal drought and ecological response to changing salinity values

Real-time Coastal Salinity Index for monitoring coastal drought and ecological response to changing salinity values

Many coastal areas are experiencing departures from normal conditions due to changing land use and climate patterns, including increased frequency, severity, or duration of floods and droughts, in some cases combinations of the two. To address these issues, the U.S. Geological Survey developed the Coastal Salinity Index (CSI) to identify and communicate fluctuating salinity conditions due to such
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Implementing FAIR practices: Storing and displaying eDNA data in the USGS Nonindigenous Aquatic Species database

We are working to incorporate environmental DNA (eDNA) data into the Nonindigenous Aquatic Species (NAS) database, which houses over 570,000 records of nonindigenous species nationally, and already is used by a broad user-base of managers and researchers regularly for invasive species monitoring. eDNA studies have allowed for the identification and biosurveillance of numerous invasive...
Implementing FAIR practices: Storing and displaying eDNA data in the USGS Nonindigenous Aquatic Species database

Implementing FAIR practices: Storing and displaying eDNA data in the USGS Nonindigenous Aquatic Species database

We are working to incorporate environmental DNA (eDNA) data into the Nonindigenous Aquatic Species (NAS) database, which houses over 570,000 records of nonindigenous species nationally, and already is used by a broad user-base of managers and researchers regularly for invasive species monitoring. eDNA studies have allowed for the identification and biosurveillance of numerous invasive and
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Enabling AI for citizen science in fish ecology

Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection...
Enabling AI for citizen science in fish ecology

Enabling AI for citizen science in fish ecology

Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This
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GrassCast: A multi-agency tool using remote sensing, modeling, and on-the-ground science to forecast grassland productivity in the Southwest

Rangeland ecosystems are one of the largest single providers of agro-ecological services in the U.S. The plant growth of these rangelands helps determine the amount of forage available for our livestock and for wildlife, as well as information about fire likelihood and restoration opportunities. However, every spring, ranchers and other rangeland managers face the same difficult...
GrassCast: A multi-agency tool using remote sensing, modeling, and on-the-ground science to forecast grassland productivity in the Southwest

GrassCast: A multi-agency tool using remote sensing, modeling, and on-the-ground science to forecast grassland productivity in the Southwest

Rangeland ecosystems are one of the largest single providers of agro-ecological services in the U.S. The plant growth of these rangelands helps determine the amount of forage available for our livestock and for wildlife, as well as information about fire likelihood and restoration opportunities. However, every spring, ranchers and other rangeland managers face the same difficult challenge —trying
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Waterbody Rapid Assessment Tool (WaterRAT): 3-dimensional Visualization of High-Resolution Spatial Data

Autonomous Underwater Vehicles (AUVs) are instruments that collect water-quality, depth, and other data in waterbodies. They produce complex and massive datasets. There is currently no standard method to store, organize, process, quality-check, analyze, or visualize this data. The Waterbody Rapid Assessment Tool (WaterRAT) is aPython application that processes and displays water-quality...
Waterbody Rapid Assessment Tool (WaterRAT): 3-dimensional Visualization of High-Resolution Spatial Data

Waterbody Rapid Assessment Tool (WaterRAT): 3-dimensional Visualization of High-Resolution Spatial Data

Autonomous Underwater Vehicles (AUVs) are instruments that collect water-quality, depth, and other data in waterbodies. They produce complex and massive datasets. There is currently no standard method to store, organize, process, quality-check, analyze, or visualize this data. The Waterbody Rapid Assessment Tool (WaterRAT) is aPython application that processes and displays water-quality data with
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Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery

In the mid-1800s, tile-drains were installed in poorly-drained soils of topographic lows as water management to protect cropland during wet conditions; consequently, estimations of tile-drain location have been based on soil series. Most tile drains are in the Midwest, however each state has farms with tile and tile-drain density has increased in the last decade. Where tile drains...
Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery

Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery

In the mid-1800s, tile-drains were installed in poorly-drained soils of topographic lows as water management to protect cropland during wet conditions; consequently, estimations of tile-drain location have been based on soil series. Most tile drains are in the Midwest, however each state has farms with tile and tile-drain density has increased in the last decade. Where tile drains quickly remove
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