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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 217 records · Page 12Linked to original sources
USGS1998

The design and performance of a low-cost strong-motion sensor using the ICS-3028 micromachined accelerometer

The severity of earthquake ground shaking varies tremendously over very short distances (Figures 1a-c). Within a distance of as little as 1 km from the nearest station, one knows little more than what can be obtained from an attenuation relation, given only distance from the fault rupture and the geology of the site. For example, if some station measures 0.5 g peak ground acceleration (PGA), then at a distance of 1 km from that site, under otherwise identical conditions, the shaking has one chance in three of being under 0.36 g or over 0.70 g, based on the curve shown in Figures la, c. Similarly, pseudovelocity (PSV) response spectra have a 5% chance of differing by 2? at 1 km distance (Figure 1 b). This variance can be the difference between moderate and severe damage. Hence, there are critical needs, both in emergency response and in mitigation (prediction of shaking strength, building codes, structural engineering), to sample ground shaking densely enough to identify individual neighborhoods suffering localized, strong shaking. These needs imply a spatially dense network of strong-motion seismographs, probably numbering thousands of sites in an urban region the size of the San Francisco Bay Area, California (Figure 1 c). It has not been economically feasible to field that many instruments, since existing ones cost many thousands of dollars apiece. For example, there are currently just a few dozen digital free-field instruments in the Bay Area. This paper is one step toward a solution to this conundrum. I demonstrate that a recently developed class of accelerometers, those constructed from silicon by 'micromachining' (a process similar to integrated circuit fabrication), is now capable of resolving ground motion with the necessary accuracy while greatly lowering both acquisition and maintenance costs.

Open-File Report↗
USGS2020

Use of real-time sensors to temporally characterize water quality in groundwater and surface water in Mason County, Illinois, 2017–19

The persistence of high nitrate concentrations in shallow groundwater has been well documented in the shallow glacial aquifer of Mason County, Illinois. Nitrates in groundwater can be a concern when concentrations exceed 10 milligrams per liter in drinking water. Additionally, nitrate in groundwater can contribute to surface water nitrogen loads that can cause increased algal growth. Algal growth increases oxygen consumption causing anoxic conditions as observed in the Gulf of Mexico Hypoxic Zone. From March 8, 2017, to March 31, 2019, groundwater level, continuous nitrate, dissolved oxygen, specific conductance, water temperature, and pH data were collected in a monitoring well to temporally assess changes in water quality using high frequency data. During this same period, instantaneous field measurements of water quality and groundwater levels were made in surface water and groundwater in and near Quiver Creek in the presumed groundwater flow path about 0.6 mile from the continuous monitoring well. Groundwater nitrate concentrations continuously measured in the aquifer during this time ranged from 14.7 to 23.2 milligrams per liter, whereas instantaneously measured nitrate concentrations in Quiver Creek ranged from 0.9 to 6.4 milligrams per liter. Nitrate concentrations measured by piezometer varied laterally and vertically in the Quiver Creek floodplain and beneath the stream. Irrigation and fertigation for agriculture is widely practiced in Mason County. This may seasonally affect the groundwater flow and movement as well as the persistence of nitrate in this area. Continuously and instantaneously measured nitrate concentrations and groundwater levels indicate that during the irrigation season, discharge to Quiver Creek from the shallow groundwater system may be limited. During and following periods when estimated irrigation use is highest, the low-nitrate deeper groundwater may be the dominant contributor to the Quiver Creek surface water, whereas during recharge events and when the system is not under the stress of irrigation, there is more contribution from the local and higher-nitrate shallow groundwater.

Illinois↗
USGS2009

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Techniques and Methods↗
USGS2021

Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.

Remote Sensing↗
USGS2010

Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

Remote Sensing↗
USGS2014

Utilizing multi-sensor fire detections to map fires in the United States

In 2006, the Monitoring Trends in Burn Severity (MTBS) project began a cooperative effort between the US Forest Service (USFS) and the U.S.Geological Survey (USGS) to map and assess burn severity all large fires that have occurred in the United States since 1984. Using Landsat imagery, MTBS is mandated to map wildfire and prescribed fire that meet specific size criteria: greater than 1000 acres in the west and 500 acres in the east, regardless of ownership. Relying mostly on federal and state fire occurrence records, over 15,300 individual fires have been mapped. While mapping recorded fires, an additional 2,700 “unknown” or undocumented fires were discovered and assessed. It has become apparent that there are perhaps thousands of undocumented fires in the US that are yet to be mapped. Fire occurrence records alone are inadequate if MTBS is to provide a comprehensive accounting of fire across the US. Additionally, the sheer number of fires to assess has overwhelmed current manual procedures. To address these problems, the National Aeronautics and Space Administration (NASA) Applied Sciences Program is helping to fund the efforts of the USGS and its MTBS partners (USFS, National Park Service) to develop, and implement a system to automatically identify fires using satellite data. In near real time, USGS will combine active fire satellite detections from MODIS, AVHRR and GOES satellites with Landsat acquisitions. Newly acquired Landsat imagery will be routinely scanned to identify freshly burned area pixels, derive an initial perimeter and tag the burned area with the satellite date and time of detection. Landsat imagery from the early archive will be scanned to identify undocumented fires. Additional automated fire assessment processes will be developed. The USGS will develop these processes using open source software packages in order to provide freely available tools to local land managers providing them with the capability to assess fires at the local level.

Conference Paper↗
USGS1983

EXPERIMENTS IN LITHOGRAPHY FROM REMOTE SENSOR IMAGERY.

Imagery from remote sensing systems such as the Landsat multispectral scanner and return beam vidicon, as well as synthetic aperture radar and conventional optical camera systems, contains information at resolutions far in excess of that which can be reproduced by the lithographic printing process. The data often require special handling to produce both standard and special map products. Some conclusions have been drawn regarding processing techniques, procedures for production, and printing limitations.

Conference Paper↗
USGS2014

Use of multi-sensor active fire detections to map fires in the United States: the future of monitoring trends in burn severity

The effort to utilize satellite-based MODIS, AVHRR, and GOES fire detections from the Hazard Monitoring System (HMS) to identify undocumented fires in Florida and improve the Monitoring Trends in Burn Severity (MTBS) mapping process has yielded promising results. This method was augmented using regression tree models to identify burned/not-burned pixels (BnB) in every Landsat scene (1984–2012) in Worldwide Referencing System 2 Path/Rows 16/40, 17/39, and 1839. The burned area delineations were combined with the HMS detections to create burned area polygons attributed with their date of fire detection. Within our study area, we processed 88,000 HMS points (2003–2012) and 1,800 Landsat scenes to identify approximately 300,000 burned area polygons. Six percent of these burned area polygons were larger than the 500-acre MTBS minimum size threshold. From this study, we conclude that the process can significantly improve understanding of fire occurrence and improve the efficiency and timeliness of assessing its impacts upon the landscape.

Conference Paper↗
USGS1980

Need for new sensors to map lithologic units

One of the most important contributions that remote sensing can make to mineral energy explorations to provide data from satellites to augment regional geological mapping. Geologic maps, which show information on the subsurface, are the main basis for formulating models of resource genesis that guide exploration. However, conventional compilation procedures are time-consuming and therefore often slow the pace of exploration, especially in large, inaccessible areas. Landsat Multispectral Scanner (MSS) images have been applied to a wide variety of specific geological problems, including discrimination of lithologic and delineation of previously unrecognized tectonic features. However, these lithologic distinctions are based on brightness, spectral reflectance, and, less commonly, the morphology of the unit, which in the wavelength region of MSS images are only rarely diagnostic of specific mineralogical content. Limonite is the only lithological material that can be identified be analyzing MSS spectral radiance.

Sixth Annual Pecora Symposium and Exposition↗
USGS2001

Validation of the USGS sensor model for topographic mapping of Venus using Magellan radar stereoimagery

The Magellan spacecraft went into Venus orbit in 1990 and by 1992 had made three complete cycles of polar orbits, each cycle covering the full range of longitudes. During this time the spacecraft obtained synthetic aperture radar (SAR) images of >96% of the planet at a resolution of 75 m/pixel. Images taken with a decreased look angle from vertical, primarily during Cycle 3, provide stereo coverage of 17% of the planet when combined with images with same-side illumination from earlier in the mission. The stereo geometry of these images is extremely favorable, allowing elevation measurements with an estimated vertical precision (EP) of ~10 m. Magellan also obtained radar altimetry data at a horizontal resolution of 10x25 km, but photogrammetric analysis of the stereoimagery can yield topographic maps with a horizontal resolution more than an order of magnitude superior to that of the altimeter.

Conference Paper↗
USGS2021

Measuring coastal acidification using in situ sensors in the National Estuary Program

Estuaries and coastal areas are highly vulnerable to the impacts of acidification on shellfish, coral reefs, fisheries, and the commercial and recreational industries that they support. Yet, little is known about the extent of this vulnerability and the estuary-specific drivers that contribute to acidification, such as nutrient enrichment from stormwater, agriculture and wastewater discharges, upwelling of CO 2 -rich seawater, elevated atmospheric CO 2 from urban and agricultural activities, benthic and marsh-driven processes, and alkalinity and carbon content of freshwater flows. Comprehensive, high resolution monitoring data are needed at varying spatial and temporal scales to provide actionable information tailored to each estuary. Because carbonate chemistry in the coastal environment can be affected by nutrient dynamics, understanding how nutrient inputs exacerbate acidification impacts is essential for the formulation of estuary-specific actions.

Alabama, California, Connecticut, Delaware. Florid↗