SEARCH · Search USGS

Results for “Sensors”

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

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14Linked to original sources

USGS2020

The Landsat Burned Area algorithm and products for the conterminous United States

Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. Here, we present the BA algorithm and products, changes relative to the BAECV algorithm and products, and updated validation metrics. We also present spatial and temporal patterns of burned area across the conterminous U.S., how burned area varies in relation to the number of operational Landsat sensors, and a comparison with other burned area datasets, including the BAECV, Monitoring Trends in Burn Severity (MTBS), GeoMAC, and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1.006 data. The BA algorithm identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984 through 2018 using machine learning, thresholding, and image segmentation. Validation with reference data from high-resolution commercial satellite imagery resulted in omission and commission error rates averaging 19% and 41%, respectively. In comparison, validation with Landsat reference data had omission and commission error rates averaging 40% and 28%, respectively when burned areas in cultivated crops and pasture/hay land-cover types were excluded. Both validation tests documented lower commission error rates relative to the BAECV products. The amount of burned area detected varies not only in response to climate but also with the number of operational sensors and scenes collected. The combined amount of burned area detected by multiple sensors was larger than from any individual sensor, but there was no significant difference between individual sensors. Therefore, we used BA products from individual sensors to assess trends over time and all available sensors to compare with other existing BA products. From 1984 through 2018, annual burned area averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. Compared to existing burned area products, the new Landsat BA products identified 29% more burned area than the BAECV products (1984–2015), 183% more than the MTBS/GeoMAC products (1984–2018), and 56% more than the MCD64A1.006 products (2003–2018). The products had similar patterns of year-to-year variability; the R2 values of linear regressions between annual burned area were >0.70 with the BAECV products and the MTBS/GeoMAC products, but somewhat lower for the MCD64A1.006 product (R2 = 0.66). The BA products are routinely produced as new Landsat data are collected and provide a unique data source to monitor and assess the spatial and temporal patterns and the impacts of fire.

Remote Sensing of Environment↗

USGS2008

Radiometric cross-calibration of the Terra MODIS and Landsat 7 ETM+ using an invariant desert site

A methodology for long-term radiometric cross-calibration between the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) sensors was developed. The approach involves calibration of near-simultaneous surface observations between 2000 and 2007. Fifty-seven cloud-free image pairs were carefully selected over the Libyan desert for this study. The Libyan desert site (+28.55??, +23.39??), located in northern Africa, is a high reflectance site with high spatial, spectral, and temporal uniformity. Because the test site covers about 12 kmx13 km, accurate geometric preprocessing is required to match the footprint size between the two sensors to avoid uncertainties due to residual image misregistration. MODIS Level IB radiometrically corrected products were reprojected to the corresponding ETM+ image's Universal Transverse Mercator (UTM) grid projection. The 30 m pixels from the ETM+ images were aggregated to match the MODIS spatial resolution (250 m in Bands 1 and 2, or 500 m in Bands 3 to 7). The image data from both sensors were converted to absolute units of at-sensor radiance and top-ofatmosphere (TOA) reflectance for the spectrally matching band pairs. For each band pair, a set of fitted coefficients (slope and offset) is provided to quantify the relationships between the testing sensors. This work focuses on long-term stability and correlation of the Terra MODIS and L7 ETM+ sensors using absolute calibration results over the entire mission of the two sensors. Possible uncertainties are also discussed such as spectral differences in matching band pairs, solar zenith angle change during a collection, and differences in solar irradiance models.

Conference Paper↗

USGS2021

Rayleigh wave amplitude uncertainty across the Global Seismographic Network and potential implications for global tomography

The Global Seismographic Network (GSN) is a multiuse, globally distributed seismic network used by seismologists, to both characterize earthquakes and study the Earth’s interior. Most stations in the network have two collocated broadband seismometers, which enable network operators to identify potential metadata and sensor issues. In this study, we investigate the accuracy with which surface waves can be measured across the GSN, by comparing waveforms of vertical‐component Rayleigh waves from M w "> M w Mw 6 and larger events between collocated sensor pairs. We calculate both the amplitude deviation and correlation coefficient between waveforms at sensor pairs. In total, we make measurements on over 670,000 event–station pairs from events that occurred from 1 January 2010 to 1 January 2020. We find that the average sensor‐pair amplitude deviation, and, therefore, GSN calibration level, is, approximately, 4% in the 25–250 s period band. Although, we find little difference in sensor‐pair amplitude deviations as a function of period across the entire network, the amount of useable data decreases rapidly as a function of increasing period. For instance, we determined that just over 12% of records at 250 s period provided useable recordings (e.g., sensor‐pair amplitude deviations of less than 20% and sensor‐pair correlation greater than 0.95). We then use these amplitude‐estimate deviations to identify how data coverage and quality could be limiting our ability to invert for whole Earth 3D attenuation models. We find an increase in the variance of our attenuation models with increasing period. For example, our degree 12 attenuation inversion at 250 s period shows 32% more variance than our degree 12 attenuation model at 25 s. This indicates that discrepancies of deep‐mantle tomography between studies could be the result of these large uncertainties. Because these high uncertainties arise from limited, high‐quality observations of long‐period ( ⁠ > 100    s ">

Bulletin of the Seismological Society of America↗

USGS2017

Evaluation of the Eureka Manta2 Water-Quality Multiprobe Sonde

Two Eureka Manta2 3.5 water-quality multiprobe sondes by Eureka Water Probes were tested at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) against known standards over the sonde operating temperatures to verify the manufacturer’s stated accuracy specifications for pH, specific conductance (SC) at 25 degrees Celsius (°C), dissolved oxygen (DO), and turbidity. The Manta2 sondes were evaluated for compliance with the USGS National Field Manual for the Collection of Water-Quality Data (NFM) criteria for continuous water-quality monitors, and for compliance with the manufacturer’s technical specifications. The Manta2 was also evaluated for its compliance to Serial Digital Interface at 1200 baud (SDI-12) version 1.3. The Manta2 met the NFM recommendations and manufacturer’s accuracy specifications for DO and turbidity at all values tested. The Manta2 pH sensors met the NFM recommendations and manufacturer’s accuracy specification for nominal pH values of 10 and lower. One of the two sensors was out of compliance by 1.2 units for pH 11.16 at 15 °C and by 0.25 unit for pH 10.78 at 40 °C. The Manta2 sensors were within the NFM recommendations for SC, except at 100 microsiemens (μS/cm) at 40 °C, where the SC sensor exceeded the test standard value by as much as 25 percent. One of two sensors was within manufacturer’s accuracy specifications at 25 °C for all the tested SC values, while the other SC sensor was outside the manufacturer’s accuracy specifications at 100 μS/cm, exceeding the test standard value by 9 percent. One of two sensors was outside the manufacturer’s accuracy specifications at 10,000 μS/cm at 15°C, exceeding the test standard value by 3 percent. One Manta2 passed SDI-12 compliance testing with a NR Systems SDI-12 Verifier. One Manta2 was field tested for 6 weeks at USGS station 02492620, National Space Technology Laboratories (NSTL) Station, Mississippi, on the Pearl River and showed overall good agreement with a well-maintained Hydrolab Datasonde 5X site sonde for water temperature, pH, and DO. Differences in SC values between the Manta2 and the site sonde were most likely due to differences in the deployment depth of the sondes.

Open-File Report↗

USGS1990

A numerical study of some potential sources of error in side-by-side seismometer evaluations

This report presents the results of a series of computer simulations of potential errors in test data, which might be obtained when conducting side-by-side comparisons of seismometers. These results can be used as guides in estimating potential sources and magnitudes of errors one might expect when analyzing real test data. First, the derivation of a direct method for calculating the noise levels of two sensors in a side-by-side evaluation is repeated and extended slightly herein. This bulk of this derivation was presented previously (see Holcomb 1989); it is repeated here for easy reference. This method is applied to the analysis of a simulated test of two sensors in a side-by-side test in which the outputs of both sensors consist of white noise spectra with known signal-tonoise ratios (SNR's). This report extends this analysis to high SNR's to determine the limitations of the direct method for calculating the noise levels at signal-to-noise levels which are much higher than presented previously (see Holcomb 1989). Next, the method is used to analyze a simulated test of two sensors in a side-by-side test in which the outputs of both sensors consist of bandshaped noise spectra with known signal-tonoise ratios. This is a much more realistic representation of real world data because the earth's background spectrum is certainly not flat. Finally, the results of the analysis of simulated white and bandshaped side-by-side test data are used to assist in interpreting the analysis of the effects of simulated azimuthal misalignment in side-by-side sensor evaluations. A thorough understanding of azimuthal misalignment errors is important because of the physical impossibility of perfectly aligning two sensors in a real world situation. The analysis herein indicates that alignment errors place lower limits on the levels of system noise which can be resolved in a side-by-side measurement It also indicates that alignment errors are the source of the fact that real data noise spectra tend to follow the earth's background spectra in shape.

Open-File Report↗

USGS1984

Experimental studies in natural groundwater recharge dynamics: Assessment of recent advances in instrumentation

To quantify and model the natural groundwater-recharge process, two sites in south-central Kansas, U.S.A., were instrumented with various modern sensors and data microloggers. The atmospheric-boundary layer and the unsaturated and saturated soil zones were monitored as a unified regime. Data from the various sensors were collected using microloggers in combination with magnetic-cassette tape, graphical and digital recorders, analog paper-tape recorders, and direct observations to evaluate and automate data collection and processing. Atmospheric sensors included an anemometer, a tipping-bucket raingage, an air-temperature thermistor, a relative-humidity probe, a net radiometer, and a barometric-pressure transducer. Sensors in the unsaturated zone consisted of soil-temperature thermocouples, tensiometers coupled with pressure transducers and dial gages, gypsum blocks, and a neutron moisture probe operated by an observer. The saturated-zone sensors consisted of a water-level pressure transducer, a conventional float gage connected to a variable potentiometer, soil thermocouples, and a number of multiple-depth piezometers. Evaluation of the operation of these sensors and recorders indicated that certain types of equipment such as pressure transducers are very sensitive to environmental conditions. Extraordinary steps had to be taken to protect some of the equipment, whereas other equipment seemed to be reliable under all conditions. Based on such experiences, a number of suggestions aimed at improving such investigations are outlined.

Kansas↗

USGS2005

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.

Remote Sensing of Environment↗

USGS2001

Design and performance of a rugged standard operative temperature thermometer for avian studies

The lack of a truly satisfactory sensor which can characterize the thermal environment at the spatial scale experienced by small endotherms has hindered study of their thermoregulatory behavior. We describe a general design for a rugged, easily constructed sensor to measure standard operative temperature, Tes. We present specific designs for adult dark-eyed juncos (Junco hyemalis) and hatchling mallards (Anas platyrhynchos). Sensor response was stable and repeatable (??1.4%) over the course of several months. Over the range of conditions for which validation data were available (variable air temperature and wind with negligible net radiation), sensors predicted the mean net heat production of live animals to within ??0.023W (equivalent to ??1??C at Tes= 15??C). The main limit on accuracy was scatter in the data on metabolism and evaporative water loss in live animals. These sensors are far more rugged and easily constructed than the heated taxidermic mounts previously used to measure Tes. These characteristics facilitate the use of significant numbers of sensors in thermal mapping studies of endotherms. ?? 2001 Elsevier Science Ltd. All rights reserved.

Journal of Thermal Biology↗

USGS2026

Strength, stiffness, and Poisson’s ratio measurements for cements used in the well completions for the extended-duration gas production test on the Alaskan North Slope

As part of an extended-duration production test from a natural gas hydrate reservoir on the Alaska North Slope, a suite of optical-fiber sensors was encased in the completion cement to provide stable downhole monitoring around each of the four project wells. How the sensors respond to changes in the reservoir over time depends in part on properties of the LiteCRETE, ArcticCem, or Tuned Light cement in which each sensor is encased, and on how well the cement bonded to the well casing, sensors, and sediment formation. Here, we report on physical properties of the cements (compressive and tensile strength, stiffness, and Poisson’s ratio), on the downhole distribution of the cements used in each well, and on the cement bond quality. Physical property measurements are presented for cements sampled during the completion activities for the three wells completed in 2022–2023. Samples were cured while being submerged in water at 2–4 °C, then tested at intervals from ∼30 days to more than one year. After an initial period of rapid increase, cement strengths largely stabilized around 120 days in all cases, meaning that cements in all wells reached their nominal strength plateau four months or more before the production test began. Calculations based on recorded volumes of pumped cement show the subpermafrost, main hole portion of each well is encased only in the higher-density “tail” cement used in that well. Downhole cement evaluation logs from two wells demonstrate how the presence of sensors and other hardware outside the casing interferes with the cement evaluation assessment. Nonetheless, the cement evaluations indicate a generally good bond with the formation and sensors over the gas hydrate reservoir intervals.

Alaska↗

USGS1997

Use of inexpensive pressure transducers for measuring water levels in wells

Frequent measurement of below ground water levels at multiple locations is an important component of many wetland ecosystem studies. These measurements, however, are usually time consuming, labor intensive, and expensive. This paper describes a water-level sensor that is inexpensive and easy to construct. The sensor is placed below the expected low water level in a shallow well and, when connected to a datalogger, uses a pressure transducer to detect groundwater or surface water elevations. Details of pressure transducer theory, sensor construction, calibration, and examples of field installations are presented. Although the transducers must be individually calibrated, the sensors have a linear response to changing water levels ( r 2 ≈ .999). Measurement errors resulting from temperature fluctuations are shown to be about 4 cm over a 35°C temperature range, but are minimal when the sensors are installed in groundwater wells where temperatures are less variable. Greater accuracy may be obtained by incorporating water temperature data into the initial calibration (0.14 cm error over a 35C temperature range). Examples of the utility of these sensors in studies of groundwater/surface water interactions and the effects of water level fluctuations on tree growth are provided.

Wetlands Ecology and Management↗

USGS2007

Downhole receiver function: A case study

Receiver function is defined as the spectral ratio of the radial component and the vertical component of the ground motion. It is used to characterize converted waves. We extend the use of the receiver function to downhole data using waves recorded in a borehole, excited by an earthquake of magnitude 4.0 near San Francisco, California, on 26 June 1994. The focal depth of the event was 6.6 km and the epicenter was located at a distance of 12.6 km from the borehole array. Six three-component sensors were located at different depths in a borehole. To extract a coherent response of the near-surface from the incoherent earthquake waves, we deconvolve the waves recorded by the sensors at different depths with the waves recorded by the sensor on the surface. Deconvolution applied to the waves in the S-time window recorded by the radial component result in an upgoing and a downgoing wave propagating with S-wave velocity. For the waves in the P-time window recorded by the radial component, deconvolution also gives an upgoing and a down-going wave propagating with S-wave velocity. This interesting result suggests a P-to-S conversion at a depth below the deepest sensor. To diagnose this we compute the receiver function for the borehole recording of the earthquake waves. The receiver function shows an upgoing wave with an arrival close to time t = 0 for the deepest sensor. The agreement of the upgoing wave in the receiver function with the travel-time curve for the P-to-S converted wave, calculated using the P- and the S-wave velocity profile, supports the hypothesis of a pronounced P-to-S conversion. We present a synthetic example to illustrate that the first arrival of the receiver function applied to borehole data gives the upward-propagating P-to-S converted wave. To corroborate the observation of the mode conversion, we apply receiver function to a different earthquake data recorded by the same borehole array in 1998. The focal depth of the event was 6.9 km and the epicenter was located at a distance of 13 km from the borehole array. The receiver function for these data also show an upgoing wave with a pulse close to time t = 0 at the deepest sensor. The moveout of the upgoing wave agrees with the travel-time curve for the P-to-S converted wave, hence supporting our observation of the mode conversion.

Bulletin of the Seismological Society of America↗

USGS2015

Potential improvements in horizontal very broadband seismic data in the IRIS/USGS component of the Global Seismic Network

The Streckeisen STS‐1 has been the primary vault‐type seismometer used in the over‐150‐station Global Seismographic Network (GSN). This sensor has long been known for its outstanding vertical, very long‐period (e.g., >100  s period), and low‐noise performance, although the horizontal long‐period noise performance is less well known. The STS‐1 is a limited, important resource, because it is no longer made or supported by the original manufacturer. We investigate the incoherent noise of horizontal‐component sensors, where coherent signals among sensors have been removed, giving an upper bound on the self‐noise of both the STS‐1 and STS‐2 horizontal components. Our findings suggest that a well‐installed STS‐2 could potentially produce data with similar or better incoherent noise levels to that of a horizontal‐component STS‐1. Along with our experimental investigation, we compare background noise levels for a calendar year at Incorporated Research Institutions for Seismology/U.S. Geological Survey network stations, which comprise approximately two‐thirds of the GSN, with collocated STS‐1 and STS‐2 seismometers. The use of an STS‐2‐class of sensor (flat to velocity to 120 s period) to acquire low‐frequency data in surface‐vault installations would allow network operators to focus more attention on improving vertical data. In order to deal with the difference in instrument response shapes between the two instruments, we detail two different time‐domain filters that would allow users to convert broadband STS‐2 data into very broadband data with a response similar to that of an STS‐1 (flat to velocity to 360 s period). We conclude that the complexity of the current primary horizontal vault sensors in the GSN may not be necessary until we are better able to isolate surface horizontal sensors from various noise sources.

Seismological Research Letters↗

USGS2008

Monitoring inland storm surge and flooding from Hurricane Ike in Texas and Louisiana, September 2008

The U.S. Geological Survey (USGS) deployed a temporary monitoring network of 117 pressure transducers (sensors) at 65 sites over an area of about 5,000 square miles to record the timing, areal extent, and magnitude of inland hurricane storm surge and coastal flooding generated by Hurricane Ike, which struck southeastern Texas and southwestern Louisiana September 12-13, 2008. Fifty-six sites were in Texas and nine were in Louisiana. Sites were categorized as surge, riverine, or beach/wave on the basis of proximity to the Gulf Coast. One-hundred five sensors from 59 sites (fig. 1) were recovered; 12 sensors from six sites either were lost during the storm or were not retrieved. All 59 sites (41 surge, 10 riverine, 8 beach/wave) had sensors to record water pressure (fig. 2), which is expressed as water level in feet above North American Vertical Datum of 1988 (NAVD88), and 46 sites had an additional sensor to record barometric pressure, expressed in pounds per square inch. Figure 3 shows an example of water level and barometric pressure over time recorded by sensors during the storm.

Louisiana, Texas↗

USGS1992

An evaluation of installation methods for STS-1 seismometers

This report documents the results of a series of experiments conducted by the authors at the Albuquerque Seismological Laboratory (ASl) during the spring and summer of 1991; the object of these experiments was to obtain and document quantitative performance comparisons of three methods of installing STS-1 seismometers. Historically, ASL has installed STS-1 sensors by cementing their thick glass base plates to the concrete floor of the vault (see Peterson and Tilgner, 1985, p 44 and Figure 31, p 51 for the details of this installation technique). This installation technique proved to be fairly satisfactory for the China Digital Seismic Network and for several sets of STS-1 sensors installed in other locations since that time. However, the cementing operation is rather labor intensive and the concrete requires a lengthy (about 1 week) curing time during which the sensor installed on it is noisy. In addition it is difficult to assure that all air bubbles have been removed from the interface between the cement and the glass base plate. If air bubbles are present beneath the plate, horizontal sensors can be unacceptably noisy. Moving a sensor installed in this manner requires the purchase of a new glass base plate because the old plate normally can not be removed without breakage. Therefore, this study was undertaken with the aim of developing an improved method of installing STS-1's. The goals were to develop a method which requires less field site labor during the installation and assures a higher quality installation when finished. In addition, the improved installation technique should promote portability. Two alternate installation techniques were evaluated in this study. One method replaces the cement between the base plate and the vault floor with sand. This method has been used in the French Geoscope program and in several IRIS/IDA installations made by the University of California at San Diego (UCSD) and possibly others. It is easily implemented in the field and is quite cheap. The other method utilizes a so called warpless housing designed by E. Wielandt and implemented at ASL. This housing is quite similar to the case design of the STS-2 sensor system. It is designed to minimize the effects of atmospheric pressure variations on the sealed housing.

Open-File Report↗

USGS2005

Evaluation of Measurements Collected with Multi-Parameter Continuous Water-Quality Monitors in Selected Illinois Streams, 2001-03

Eight streams, representing a wide range of environmental and water-quality conditions across Illinois, were monitored from July 2001 to October 2003 for five water-quality parameters as part of a pilot study by the U.S. Geological Survey (USGS) in cooperation with the Illinois Environmental Protection Agency (IEPA). Continuous recording multi-parameter water-quality monitors were installed to collect data on water temperature, dissolved-oxygen concentrations, specific conductivity, pH, and turbidity. The monitors were near USGS streamflow-gaging stations where stage and streamflow are continuously recorded. During the study period, the data collected for these five parameters generally met the data-quality objectives established by the USGS and IEPA at all eight stations. A similar pilot study during this period for measurement of chlorophyll concentrations failed to achieve the data-quality objectives. Of all the sensors used, the temperature sensors provided the most accurate and reliable measurements (generally within ?5 percent of a calibrated thermometer reading). Signal adjustments and calibration of all other sensors are dependent upon an accurate and precise temperature measurement. The dissolved-oxygen sensors were the next most reliable during the study and were responsive to changing conditions and accurate at all eight stations. Specific conductivity was the third most accurate and reliable measurement collected from the multi-parameter monitors. Specific conductivity at the eight stations varied widely-from less than 40 microsiemens (?S) at Rayse Creek near Waltonville to greater than 3,500 ?S at Salt Creek at Western Springs. In individual streams, specific conductivity often changed quickly (greater than 25 percent in less than 3 hours) and the sensors generally provided good to excellent record of these variations at all stations. The widest range of specific-conductivity measurements was in Salt Creek at Western Springs in the Greater Chicago metropolitan area. Unlike temperature, dissolved oxygen, and specific conductivity that have been typically measured over a wide range of historical streamflow conditions in many streams, there are few historical turbidity data and the full range of turbidity values is not well known for many streams. Because proposed regional criteria for turbidity in regional streams are based on upper 25th percentiles of concentration in reference streams, accurate determination of the distribution of turbidity in monitored streams is important. Digital data from all five sensors were recorded within each of the eight sondes deployed in the streams and in automated data recorders in the nearby streamflow-gaging houses at each station. The data recorded on each sonde were retrieved to a field laptop computer at each station visit. The feasibility of transmitting these data in near-real time to a central processing point for dissemination on the World-Wide Web was tested successfully. Data collected at all eight stations indicate that a number of factors affect the dissolved-oxygen concentration in the streams and rivers monitored. These factors include: temperature, biological activity, nutrient runoff, and weather (storm runoff). During brief periods usually in late summer, dissolved-oxygen concentrations in half or more of the eight streams and rivers monitored were below the 5 milligrams per liter minimum established by the Illinois Pollution Control Board to protect aquatic life. Because the streams monitored represent a wide range in water-quality and environmental conditions, including diffuse (non-point) runoff and wastewater-effluent contributions, this result indicates that deleterious low dissolved-oxygen concentrations during late summer may be widespread in Illinois streams.

Scientific Investigations Report↗

USGS2024

Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane

The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r 2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r 2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.

Coastal Engineering↗

USGS2013

Radiometric and geometric assessment of data from the RapidEye constellation of satellites

To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface using imagery acquired from multiple spaceborne imaging sensors. The RapidEye (RE) satellite constellation acquires high-resolution satellite images covering the entire globe within a very short period of time by sensors identical in construction and cross-calibrated to each other. To evaluate the RE high-resolution Multi-spectral Imager (MSI) sensor capabilities, a cross-comparison between the RE constellation of sensors was performed first using image statistics based on large common areas observed over pseudo-invariant calibration sites (PICS) by the sensors and, second, by comparing the on-orbit radiometric calibration temporal trending over a large number of calibration sites. For any spectral band, the individual responses measured by the five satellites of the RE constellation were found to differ <2–3% from the average constellation response depending on the method used for evaluation. Geometric assessment was also performed to study the positional accuracy and relative band-to-band (B2B) alignment of the image data sets. The position accuracy was assessed by comparing the RE imagery against high-resolution aerial imagery, while the B2B characterization was performed by registering each band against every other band to ensure that the proper band alignment is provided for an image product. The B2B results indicate that the internal alignments of these five RE bands are in agreement, with bands typically registered to within 0.25 pixels of each other or better.

International Journal of Remote Sensing↗

USGS2021

Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise

Tilting of the ground due to loading by the variable atmosphere is known to corrupt very long period horizontal seismic records (below 10 mHz) even at the quietest stations. At BFO (Black Forest Observatory, SW-Germany), the opportunity arose to study these disturbances on a variety of simultaneously operated state-of-the-art broad-band sensors. A series of time windows with clear atmospherically caused effects was selected and attempts were made to model these ‘signals’ in a deterministic way. This was done by simultaneously least-squares fitting the locally recorded barometric pressure and its Hilbert transform to the ground accelerations in a bandpass between 100 and 3600 s periods. Variance reductions of up to 97 per cent were obtained. We show our results by combining the ‘specific pressure induced accelerations’ for the two horizontal components of the same sensor as vectors on a horizontal plane, one for direct pressure and one for its Hilbert transform. It turned out that at BFO the direct pressure effects are large, strongly position dependent and largely independent of atmospheric events for instruments installed on piers, while three post-hole sensors are only slightly affected. The infamous ‘cavity effects’ are invoked to be responsible for these large effects on the pier sensors. On the other hand, in the majority of cases all sensors showed very similar magnitudes and directions for the vectors obtained for the regression with the Hilbert transform, but highly variable from event to event especially in direction. Therefore, this direction most certainly has to do with the gradient of the pressure field moving over the station which causes a larger scale deformation of the crust. The observations are very consistent with these two fundamental mechanisms of how fluctuations of atmospheric surface pressure causes tilt noise. The results provide a sound basis for further improvements of the models for these mechanisms. The methods used here can already help to reduce atmospherically induced noise in long-period horizontal seismic records.

Geophysical Journal International↗