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At least 55 records · Page 3Linked to original sources
USGS2019

Manufacturing simple and inexpensive soil surface temperature and gravimetric water content sensors

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. However, at the soil surface these variables are highly dynamic and standard sensors do not explicitly measure temperature or moisture in the upper few millimeters of the soil profile. This paper describes methods for manufacturing simple, inexpensive sensors that simultaneously measure the temperature and moisture of the upper 5 mm of the soil surface. In addition to sensor construction, steps for quality control, as well as for calibration for various substrates, are explained. The sensors incorporate a Type E thermocouple to measure temperature and assess soil moisture by measuring the resistance between two gold-plated metal probes at the end of the sensor at a depth of 5 mm. The methods presented here can be altered to customize probes for different depths or substrates. These sensors have been effective in a variety of environments and have endured months of heavy rains in tropical forests as well as intense solar radiation in deserts of the southwestern U.S. Results demonstrate the effectiveness of these sensors for evaluating warming, drying, and freezing of the soil surface in a global change experiment.

Journal of Visualized Experiments↗
USGS2022

In situ soil moisture sensors in undisturbed soils

Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture data has been slow due to inconsistent data collection, poor standardization, and typically short record duration. Soil moisture, or quantitatively volumetric soil water content (SWC), is measured using buried, in situ sensors that infer SWC from an electromagnetic response. This signal can vary considerably with local site conditions such as clay content and mineralogy, soil salinity or bulk electrical conductivity, and soil temperature; each of these can have varying impacts depending on the sensor technology., Furthermore, poor soil contact and sensor degradation can affect the quality of these readings over time. Unlike more traditional environmental sensors, there are no accepted standards, maintenance practices, or quality controls for SWC data. As such, SWC is a challenging measurement for many environmental monitoring networks to implement. Here, we attempt to establish a community-based standard of practice for in situ SWC sensors so that future research and applications have consistent guidance on site selection, sensor installation, data interpretation, and long-term maintenance of monitoring stations., The videography focuses on a multi-agency consensus of best-practices and recommendations for the installation of in situ SWC sensors. This paper presents an overview of this protocol along with the various steps essential for high-quality and long-term SWC data collection. This protocol will be of use to scientists and engineers hoping to deploy a single station or an entire network.

Journal of Visualized Experiments↗
USGS2003

Continuous measurement of suspended-sediment discharge in rivers by use of optical backscatterance sensors

Optical sensors have been used to measure turbidity and suspended-sediment concentration by many marine and estuarine studies, and optical sensors can provide automated, continuous time series of suspended-sediment concentration and discharge in rivers. Three potential problems with using optical sensors are biological fouling, particle-size variability, and particle-reflectivity variability. Despite varying particle size, output from an optical backscatterance sensor in the Sacramento River at Freeport, California, USA, was calibrated successfully to discharge-weighted, cross-sectionally averaged suspended-sediment concentration, which was measured with the equal discharge-, or width-increment, methods and an isokinetic sampler. A correction for sensor drift was applied to the 3-year time series. However, the calibration of an optical backscatterance sensor used in the Colorado River at Cisco, Utah, USA, was affected by particle-size variability. The adjusted time series at Freeport was used to calculate hourly suspended-sediment discharge that compared well with daily values from a sediment station at Freeport. The appropriateness of using optical sensors in rivers should be evaluated on a site-specific basis and measurement objectives, potential particle size effects, and potential fouling should be considered.

California, Utah↗
USGS2023

Automated soft pressure sensor array-based sea lamprey detection using machine learning

Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.

IEEE Sensors Journal↗
USGS2019

Screen-printed soft capacitive sensors for spatial mapping of both positive and negative pressures

Soft pressure sensors are one class of the essential devices for robotics and wearable device applications. Despite the tremendous progress, sensors that can reliably detect both positive and negative pressures have not yet been demonstrated. In this paper, we report a soft capacitive pressure sensor made using a convenient and low-cost screen-printing process that can reliably detect both positive and negative pressures from −60 kPa to 20 kPa. The sensor is made with an Ecoflex-0030 dielectric layer, conductive and stretchable poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) (with ionic additives) electrodes and polydimethylsiloxane (PDMS) encapsulation layers. Air gaps are designed and incorporated into the dielectric layer to significantly enhance the sample deformation and pressure response especially to negative pressure. The sensor exhibits repeatable response for thousands of cycles, even under bending or stretching conditions. Lastly, to demonstrate the practical application, a 12×12-pixel sensor array that can automatically measure both positive and negative pressure distributions has been reported under −20 kPa and 10 kPa.

Advanced Functional Materials↗
USGS2010

An overview of sensor calibration inter-comparison and applications

Long-term climate data records (CDR) are often constructed using observations made by multiple Earth observing sensors over a broad range of spectra and a large scale in both time and space. These sensors can be of the same or different types operated on the same or different platforms. They can be developed and built with different technologies and are likely operated over different time spans. It has been known that the uncertainty of climate models and data records depends not only on the calibration quality (accuracy and stability) of individual sensors, but also on their calibration consistency across instruments and platforms. Therefore, sensor calibration inter-comparison and validation have become increasingly demanding and will continue to play an important role for a better understanding of the science product quality. This paper provides an overview of different methodologies, which have been successfully applied for sensor calibration inter-comparison. Specific examples using different sensors, including MODIS, AVHRR, and ETM+, are presented to illustrate the implementation of these methodologies.

Frontiers of Earth Science in China↗
USGS2020

Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States

Satellite-derived phenology metrics are valuable tools for understanding broad-scale patterns and changes in vegetated landscapes over time. However, the extraction and interpretation of phenology in ecosystems with subtle growth dynamics can be challenging. US National Park Service monitoring of evergreen pinyon-juniper ecosystems in the western US revealed an unexpected winter-peaking phenological pattern in normalized difference vegetation index (NDVI) time-series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. In this paper, we assess the validity of the winter peaks through ground-based observation of phenology and examination of solar and satellite geometry effects. To test the premise of a true vegetation response, we analyzed NDVI values extracted from a time series of ground-based digital camera (‘phenocam’) images collected September 2017 to December 2018 in a pinyon-juniper woodland in Arizona, US. Results show pinyon and juniper growth peaked in the warm season, as did the other species in the phenocam field of view. NDVI time series from four other sensors (Landsat 7, Sentinel-2, VIIRS, and Proba-V) confirmed that winter peaks in this ecosystem are not limited to MODIS products. Examination of NDVI time series (2003–2018) derived from daily 250-m MODIS data in the broader pinyon-juniper ecosystem revealed that solar-to-sensor angle, sensor zenith angle, and forward/back-scatter reflectance explained >80% of intra-annual variability. Solar-to-sensor angle exerted the greatest control, and the direction of its correlation (positive) was the opposite of that which would be expected if it were driven by vegetation greenness. Solar-to-sensor angle is controlled seasonally by solar zenith angle and daily by variations in satellite overpass geometry. We mapped winter peaks across the western US in Google Earth Engine using 500-m MODIS MCD43A4 data, which correct for reflectance differences caused by view angle. In areas where winter vegetation peaks are ecologically improbable (i.e., locations with sub-freezing December temperatures), consistent winter peaks (≥ 14 years in 2003 to 2018) are widespread in both pinyon-juniper and non-pinyon-juniper conifer ecosystems; winter peaks are common (≥ 5 years in 2003 to 2018) across areas of shrubland. We attribute winter peaks to the positive correlation of NDVI with solar-to-sensor angle and solar zenith angle in combination with sparse, vertically oriented evergreen vegetation canopies. Increasing shadow visibility has been shown to increase overall NDVI, and the prevalence of the winter peaking in evergreen western sparse canopy ecosystems is consistent with this hypothesis. The extent of winter peaking patterns may have been previously overlooked due to temporal compositing, curve fitting, and incomplete snow screening.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2014

Sensor data as a measure of native freshwater mussel impact on nitrate formation and food digestion in continuous-flow mesocosms

Native freshwater mussels can influence the aquatic N cycle, but the mechanisms and magnitude of this effect are not fully understood. We assessed the effects of Amblema plicata and Lampsilis cardium on N transformations over 72 d in 4 continuous-flow mesocosms, with 2 replicates of 2 treatments (mesocosms with and without mussels), equipped with electronic water-chemistry sensors. We compared sensor data to discrete sample data to assess the effect of additional sensor measurements on the ability to detect mussel-related effects on NO 3 – formation. Analysis of 624 sensor-based data points detected a nearly 6% increase in NO 3 – concentration in overlying water of mesocosms with mussels relative to mesocosms without mussels (p < 0.05), whereas analysis of 36 discrete samples showed no statistical difference in NO 3 – between treatments. Mussels also significantly increased NO 2 – concentrations in the overlying water, but no significant difference in total N was observed. We used the sensor data for phytoplankton-N and NH 4 + to infer that digestion times in mussels were 13 ± 6 h. The results suggest that rapid increases in phytoplankton-N levels in the overlying water can lead to decreased lag times between phytoplankton-N and NH 4 + maxima. This result indicates that mussels may adjust their digestion rates in response to increased levels of food. The adjustment in digestion time suggests that mussels have a strong response to food availability that can disrupt typical circadian rhythms. Use of sensor data to measure directly and to infer mussel effects on aquatic N transformations at the mesocosm scale could be useful at larger scales in the future.

Freshwater Science↗
USGS2009

TinyOS-based quality of service management in wireless sensor networks

Previously the cost and extremely limited capabilities of sensors prohibited Quality of Service (QoS) implementations in wireless sensor networks. With advances in technology, sensors are becoming significantly less expensive and the increases in computational and storage capabilities are opening the door for new, sophisticated algorithms to be implemented. Newer sensor network applications require higher data rates with more stringent priority requirements. We introduce a dynamic scheduling algorithm to improve bandwidth for high priority data in sensor networks, called Tiny-DWFQ. Our Tiny-Dynamic Weighted Fair Queuing scheduling algorithm allows for dynamic QoS for prioritized communications by continually adjusting the treatment of communication packages according to their priorities and the current level of network congestion. For performance evaluation, we tested Tiny-DWFQ, Tiny-WFQ (traditional WFQ algorithm implemented in TinyOS), and FIFO queues on an Imote2-based wireless sensor network and report their throughput and packet loss. Our results show that Tiny-DWFQ performs better in all test cases. ?? 2009 IEEE.

Conference Paper↗
USGS2010

Operational calibration and validation of landsat data continuity mission (LDCM) sensors using the image assessment system (IAS)

Systematic characterization and calibration of the Landsat sensors and the assessment of image data quality are performed using the Image Assessment System (IAS). The IAS was first introduced as an element of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) ground segment and recently extended to Landsat 4 (L4) and 5 (L5) Thematic Mappers (TM) and Multispectral Sensors (MSS) on-board the Landsat 1-5 satellites. In preparation for the Landsat Data Continuity Mission (LDCM), the IAS was developed for the Earth Observer 1 (EO-1) Advanced Land Imager (ALI) with a capability to assess pushbroom sensors. This paper describes the LDCM version of the IAS and how it relates to unique calibration and validation attributes of its on-board imaging sensors. The LDCM IAS system will have to handle a significantly larger number of detectors and the associated database than the previous IAS versions. An additional challenge is that the LDCM IAS must handle data from two sensors, as the LDCM products will combine the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) spectral bands.

Conference Paper↗
USGS2001

Landsat 7 thermal-IR image sharpening using an artificial neural network and sensor model

The enhanced thematic mapper (plus) (ETM+) instrument on Landsat 7 shares the same basic design as the TM sensors on Landsats 4 and 5, with some significant improvements. In common are six multispectral bands with a 30-m ground-projected instantaneous field of view (GIFOV). However, the thermaL-IR (TIR) band now has a 60-m GIFOV, instead of 120-m. Also, a 15-m panchromatic band has been added. The artificial neural network (NN) image sharpening method described here uses data from the higher spatial resolution ETM+ bands to enhance (sharpen) the spatial resolution of the TIR imagery. It is based on an assumed correlation over multiple scales of resolution, between image edge contrast patterns in the TIR band and several other spectral bands. A multilayer, feedforward NN is trained to approximate TIR data at 60m, given degraded (from 30-m to 60-m) spatial resolution input from spectral bands 7, 5, and 2. After training, the NN output for full-resolution input generates an approximation of a TIR image at 30-m resolution. Two methods are used to degrade the spatial resolution of the imagery used for NN training, and the corresponding sharpening results are compared. One degradation method uses a published sensor transfer function (TF) for Landsat 5 to simulate sensor coarser resolution imagery from higher resolution imagery. For comparison, the second degradation method is simply Gaussian low pass filtering and subsampling, wherein the Gaussian filter approximates the full width at half maximum amplitude characteristics of the TF-based spatial filter. Two fixed-size NNs (that is, number of weights and processing elements) were trained separately with the degraded resolution data, and the sharpening results compared. The comparison evaluates the relative influence of the degradation technique employed and whether or not it is desirable to incorporate a sensor TF model. Preliminary results indicate some improvements for the sensor model-based technique. Further evaluation using a higher resolution reference image and strict application of sensor model to data is recommended.

Conference Paper↗
USGS2007

Consistency of L4 TM absolute calibration with respect to the L5 TM sensor based on near-simultaneous image acquisition

The Landsat archive provides more than 35 years of uninterrupted multispectral remotely sensed data of Earth observations. Since 1972, Landsat missions have carried different types of sensors, from the Return Beam Vidicon (RBV) camera to the Enhanced Thematic Mapper Plus (ETM+). However, the Thematic Mapper (TM) sensors on Landsat 4 (L4) and Landsat 5 (L5), launched in 1982 and 1984 respectively, are the backbone of an extensive archive. Effective April 2, 2007, the radiometric calibration of L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) was updated to use an improved lifetime gain model, based on the instrument's detector response to pseudo-invariant desert site data and cross-calibration with the L7 ETM+. However, no modifications were ever made to the radiometric calibration procedure of the Landsat 4 (L4) TM data. The L4 TM radiometric calibration procedure has continued to use the Internal Calibrator (IC) based calibration algorithms and the post calibration dynamic ranges, as previously defined. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The number of coincident image pairs in the USGS EROS archive is limited, so the scene selection for the cross-calibration studies proved to be a challenge. Additionally, because of the lack of near-simultaneous images available over well-characterized and traditionally used calibration sites, alternate sites that have high reflectance, large dynamic range, high spatial uniformity, high sun elevation, and minimal cloud cover were investigated. The alternate sites were identified in Yuma, Iraq, Egypt, Libya, and Algeria. The cross-calibration approach involved comparing image statistics derived from large common areas observed eight days apart by the two sensors. This paper summarizes the average percent differences in reflectance estimates obtained between the two sensors. The work presented in this paper is a first step in understanding the current performance of L4 TM absolute calibration and potentially serves as a platform to revise and improve the radiometric calibration procedures implemented for the processing of L4 TM data.

Conference Paper↗
USGS2010

Monitoring the long term stability of the IRS-P6 AWiFS sensor using the Sonoran and RVPN sites

This paper focuses on radiometric and geometric assessment of the Indian Remote Sensing (IRS-P6) Advanced Wide Field Sensor (AWiFS) sensor using the Sonoran desert and Railroad Valley Playa, Nevada (RVPN) ground sites. Imageto- Image (I2I) accuracy and relative band-to-band (B2B) accuracy were measured. I2I accuracy of the AWiFS imagery was assessed by measuring the imagery against Landsat Global Land Survey (GLS) 2000. The AWiFS images were typically registered to within one pixel to the GLS 2000 mosaic images. The B2B process used the same concepts as the I2I, except instead of a reference image and a search image; the individual bands of a multispectral image are tested against each other. The B2B results showed that all the AWiFS multispectral bands are registered to sub-pixel accuracy. Using the limited amount of scenes available over these ground sites, the reflective bands of AWiFS sensor indicate a long-term drift in the top-of-atmosphere (TOA) reflectance. Because of the limited availability of AWiFS scenes over these ground sites, a comprehensive evaluation of the radiometric stability using these sites is not possible. In order to overcome this limitation, a cross-comparison between AWiFS and Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) was performed using image statistics based on large common areas observed by the sensors within 30 minutes. Regression curves and coefficients of determination for the TOA trends from these sensors were generated to quantify the uncertainty in these relationships and to provide an assessment of the calibration differences between these sensors.

Conference Paper↗
USGS2017

Repeatability of testing a small broadband sensor in the Albuquerque Seismological Laboratory Underground Vault

Variability in seismic instrumentation performance plays a fundamental role in our ability to carry out experiments in observational seismology. Many such experiments rely on the assumed performance of various seismic sensors as well as on methods to isolate the sensors from nonseismic noise sources. We look at the repeatability of estimating the self‐noise, midband sensitivity, and the relative orientation by comparing three collocated Nanometrics Trillium Compact sensors. To estimate the repeatability, we conduct a total of 15 trials in which one sensor is repeatedly reinstalled, alongside two undisturbed sensors. We find that we are able to estimate the midband sensitivity with an error of no greater than 0.04% with a 99th percentile confidence, assuming a standard normal distribution. We also find that we are able to estimate mean sensor self‐noise to within ±5.6 dB with a 99th percentile confidence in the 30–100‐s‐period band. Finally, we find our relative orientation errors have a mean difference in orientation of 0.0171° from the reference, but our trials have a standard deviation of 0.78°.

Bulletin of the Seismological Society of America↗
USGS2018

Laboratory tests of three Z‐Land Fairfield Nodal 5‐Hz, three‐component sensors

We conduct a number of laboratory tests at the Albuquerque Seismological Laboratory to verify the self‐noise and fidelity in which 3 three‐component Fairfield Nodal Z‐Land, Generation 2, 5‐Hz sensors are able to record seismic signals. In addition to the incoherent self‐noise of the sensors, we estimate the sensitivity of the units in digital volts/m/s, the damping, and the free period. These three parameters allow us to completely characterize the response of the instruments. We find that the responses of all components match a mean‐derived response to within 5% of amplitude and 0.03 radians in phase. This close agreement suggests that for most applications a nominal response is suitable. We also checked the timing of the units as compared to a Quanterra Q330HR and found good agreement up to 200samples/s . Finally, we compared the results of our noise tests on these sensors to a couple of nodal sensors recently deployed at the Community Wavefield Demonstration Experiment in north‐central Oklahoma and found that local site noise and not the sensor self‐noise is a fundamental limiter in the resolution of these deployed sensors at frequencies above ∼0.1Hz .

Seismological Research Letters↗
USGS2014

A Global Lake Ecological Observatory Network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate process- based ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest.

Inland Waters↗
USGS2022

San Francisco Estuary chlorophyll sensor and sample analysis intercomparison

This report presents an assessment of chlorophyll collection methods and anonymous results of field and laboratory comparisons in 2018 - 2019 by agencies in the San Francisco Estuary (SFE). The methods assessment and comparison exercises, with funding provided by the Delta Regional Monitoring Program and Bay Nutrient Management Strategy and in-kind contributions from participating agencies, are a first step to facilitate future comparisons and syntheses of data and inform best science practices in the region. In situ sonde comparison exercises found general agreement between two models of Yellow Springs Instrument (YSI) sensors, but the newer sensor (EXO v2 - total algae) measured higher chlorophyll fluorescence (fCHL) relative to the older YSI sensor (6-series 6025). Results may be attributed to the use of a two-point calibration and the fluorescence response of algal cultures in sensor development by the manufacturer. The laboratory comparison included participation by 12 distinct field - laboratory pairs (or groups), with one group analyzing filters using two analytical methods. Filters were collected in triplicate across three sampling events in 2018, and all sample results were pooled together. Results of statistical analyses indicated that nominal filter pore size, the grinding method associated with pigment extraction, and analytical methods do not introduce variability to the chlorophyll-a measurement (Chl-a). When Chl-a results were assessed by sample event, however, significant differences between nominal pore size and analytical methods existed; these differences could be attributed to the small sample size per event. Consistent reporting units and high-concentration calibration standards for field sensors among data collection agencies would improve the consistency and comparability of data collected in the SFE. More routine split sampling events, longer term sensor comparison exercises, and further processing and analytical comparisons that control for individual filterers may also enhance comparability in the region.

California↗
USGS1988

Description and testing of three moisture sensors for measuring surface wetness on carbonate building stones

Three moisture sensors were tested as a means for determining the surface wetness on carbonate building stones exposed to conditions that produce deposition of moisture. A relative-humidity probe, a gypsum-coated circuit grid, and a limestone-block resistor were tested as sensors for determining surface wetness. Sensors were tested under laboratory conditions of constant relative humidity and temperature and also under on-site conditions of variable relative humidity and temperature for eight weeks at Newcomb, NY. Laboratory tests indicated that relative humidity alone did not cause sensors to become saturated with water. However, the rates of drying indicated by the sensors after an initial saturation were inversely related to the relative humidity. On-site testing of the relative-humidity probe and the gypsum-coated ciruit grid indicated that they respond to a diurnal wetting and drying cycle; the limestone-block resistor responded only to rainfall.

Review of Scientific Instruments↗