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Miscellaneous methods for measuring matric or water potential
A variety of techniques to measure matric potential or water potential in the laboratory and in the field are described in this section. The techniques described herein require equilibration of some medium whose matric or water potential can be determined from previous calibration or can be measured directly. Under equilibrium conditions the matric or water potential of the medium is equal to that of the soil. The techniques can be divided into: (i) those that measure matric potential and (ii) those that measure water potential (sum of matric and osmotic potentials). Matric potential is determined when the sensor matrix is in direct contact with the soil, so salts are free to diffuse in or out of the sensor matrix, and the equilibrium measurement therefore reflects matric forces acting on the water. Water potential is determined when the sensor is separated from the soil by a vapor gap, so salts are not free to move in or out of the sensor, and the equilibrium measurement reflects the sum of the matric and osmotic forces acting on the water. Seven different techniques are described in this section. Those that measure matric potential include (i) heat dissipation sensors, (ii) electrical resistance sensors, (iii) frequency domain and time domain sensors, and (iv) electro-optical switches. A method that can be used to measure matric potential or water potential is the (v) filter paper method. Techniques that measure water potential include (vi) the Dew Point Potentiameter (Decagon Devices, Inc., Pullman, WA1) (water activity meter) and (vii) vapor equilibration. The first four techniques are electronically based methods for measuring matric potential. Heat dissipation sensors and electrical resistance sensors infer matric potential from previously determined calibration relations between sensor heat dissipation or electrical resistance and matric potential. Frequency-domain and timedomain matric potential sensors measure water content, which is related to matric potential of the sensor through calibration. Electro-optical switches measure changes in light transmission through thin, nylon filters as they absorb or desorb water in response to changes in matric potential. Heat dissipation sensors and electrical resistance sensors are used primarily in the field to provide information on matric potential. Frequency domain matric potential sensors are new and have not been widely used. Time domain matric potential sensors and electro-optical switches are new and have not been commercialized. For the fifth technique, filter paper is used as the standard matrix. The filter paper technique measures matric potential when the filter paper is in direct contact with soil or water potential when separated from soil by a vapor gap. The Dew Point Potentiameter calculates water potential from the measured dew point and sample temperature. The vapor equilibration technique involves equilibration of soil samples with salt solutions of known osmotic potential. The filter paper, Dew Point Potentiameter, and vapor equilibration techniques are generally used in the laboratory to measure water potential of disturbed field samples or to measure water potential for water retention functions.
Relative performance of several inexpensive accelerometers
We examined the performance of several low-cost accelerometers for highly cost-driven applications in recording earthquake strong motion. We anticipate applications for such sensors in providing the lifeline and emergency-response communities with an immediate, comprehensive picture of the extent and characteristics of likely damage. We also foresee their use as 'filler' instruments sited between research-grade instruments to provide spatially detailed and near-field records of large earthquakes (on the order of 1000 stations at 600-m intervals in San Fernando Valley, population 1.2 million, for example). The latter applications would provide greatly improved attenuation relationships for building codes and design, the first examples of mainshock information (that is, potentially nonlinear regime) for microzonation, and a suite of records for structural engineers. We also foresee possible applications in monitoring structural inter-story drift during earthquakes, possibly leading to local and remote alarm functions as well as design criteria. This effort appears to be the first of its type at the USGS. It is spurred by rapid advances in sensor technology and the recognition of potential non-classical applications. In this report, we estimate sensor noise spectra, relative transfer functions and cross-axis sensitivity of six inexpensive sensors. We tested three micromachined ('silicon-chip') sensors in addition to classical force-balance and piezoelectric examples. This sample of devices is meant to be representative, not comprehensive. Sensor noise spectra were estimated by recording system output with the sensor mounted on a pneumatically supported 545-kg optical-bench isolation table. This isolation table appears to limit ground motion to below our system noise level. These noise estimates include noise introduced by signal-conditioning circuitry, the analog-to-digital converter (ADC), and noise induced in connecting wiring by ambient electromagnetic fields in our suburban laboratory. These latter sources are believed to dominate sensor noise in the quieter sensors we tested. Transfer functions were obtained relative to a research grade force-balance accelerometer (a Kinemetrics TM FBA-11) by shaking the sensors simultaneously on the same shake table and taking spectral ratios with the output of the FBA- 11. This reference sensor is said to have 120 db dynamic range (-+20 bits, though we only digitized it at 16 bits resolution and drove it with relatively small signals). We did not test temperature sensitivity, which is thought to be a significant issue at least for the silicon devices. Though these tests were not designed to be definitive (our anticipated applications do not demand research-grade precision), our tests do appear to have been successful in estimating relative transfer functions from about 0.3 to 50 Hz. Most sensors performed adequately in this range, with essentially fiat relative transfer functions. Noise tests appear to measure sensor noise well for the noisier (generally less expensive) instruments from about 0.1 to 50 Hz.
Experimental investigations regarding the use of sand as an inhibitor of air convection in deep seismic boreholes
Tilt has been the nemesis of horizontal long period seismology since its inception. Modern horizontal long period seismometers with their long natural periods are incredibly sensitive to tilt. They can sense tilts smaller than 10 -11 radians. To most readers, this is just a very very small number, so we will begin with an example, which should help to illustrate just how small 10 -11 radians is. Suppose we have an absolutely rigid rod which is approximately 4170 kilometers long; this just happens to be the Rand McNally map scaled crow flight distance between Los Angeles and Boston. Tilting this rod 10 -11 radians corresponds to raising one end of the rod 0.0000417 meters. Alas, this is just another very very small number! However, this corresponds to slipping a little less than one third a sheet of ordinary copying paper under one end of this perfectly rigid rod. To clarify, we mean, take a sheet of paper just like the paper this report is printed on and split it a little less than one third in the thickness direction, then put it under the end of the 4170 kilometer long rod! This will tilt the rod 10 -11 radians. Real world seismometers are nowhere near the length of this rod. A KS-54000 is about two meters long. Tilting a rod only two meters long 10~n radians corresponds to moving one end of this rod a mere 0.00000000002 meters or 0.02 millimicrons. As one of the authors old math teachers used to say, "That's PDS" (PDS = Pretty Damn Small). Unfortunately, the long period seismologist does not have the luxury of ignoring PDS numbers when it suits him as the mathematician frequently does. He must live in the real world in which tilts this small create severe contamination of long period seismic data. At periods longer than 20 seconds, tilt noise contaminates the long period data from all instruments installed on or near the earth's surface. Many years of experimentation revealed that installing the sensors at depth in deep mines drastically reduced the level of tilt noise in long period data. However, low levels of tilt noise persisted even at great depth; this noise was caused by air convection in the vault in which the sensors were installed. Over the years, methods were developed to control the air motion with mechanical barriers (boxes) around the sensors and by stratifying (creating a situation in which the air temperature increases with height) the air in the vault near the seismometer. These methods decreased tilt noise in deep mines to very low levels. However, deep mines, that are economically and environmentally suitable and accessible to seismology, are not plentiful and are not evenly distributed over the earth's surface. Therefore, the borehole deployable Teledyne Geotech KS-36000 and later the KS-54000 sensor systems were developed to fulfill the need for instruments that could be installed at depth wherever high quality long period data was desired. Early in the development program, it became evident to the Teledyne Geotech personnel that air convection within the borehole was going to be a significant problem in KS deployments. Experimental and theoretical investigations conducted by Teledyne Geotech (see Douze and Sherwin, 1975, and Sherwin and Cook, 1976) produced a list of recommended installation procedures for reducing the effects of air convection. These procedures consisted of wrapping the sensor in a relatively thin layer of foam insulation, filling the free space volume in the vicinity of the centralizer-bail assembly with foam insulation, and the installation of styrofoam hole plugs immediately above the cable strain relief assembly at the top of the sensor package and at the top of the borehole. This technology has performed quite satisfactorily for over 20 years but evidence of tilt noise in the system output has persisted throughout the KS deployment program (the evidence was that the horizontal components were usually noisier than the vertical components) even in deep boreholes. Some deep borehole sites have been plagued by quite high levels of horizontal noise. Therefore, there has been a definite need for a new technique for controlling low level tilt noise in deep boreholes and the use of sand has been under consideration for several years. Figure 1 contains conceptual illustrations of both the conventional holelock installed KS sensor system and the same sensor installed in sand. This figure demonstrates the major differences between the two installation methods. The curved arrows in the borehole on the left in the figure denote possible air convection cells which are believed to be the source of tilt noise in some of the conventional installations. This air motion is eliminated in a sand installation by filling most of the free air volume surrounding the seismometer with sand as shown in the right hand portion of the figure. The sand actually performs two functions; it prevents air motion and provides a remarkably ridgid clamping of the seismometer in the borehole. This report presents the results of quantitative experimental investigations into the effectiveness of controlling low level air convection in seismic borehole installations with sand. The main body of the experimental effort consisted of installing two KS-540001 sensor systems in closely spaced shallow boreholes, allowing the sensors to reach equilibrium operation, and then pouring sand into both boreholes to observe any changes caused by pouring sand into the holes. The hypothesis of the experiment was that the sand would fill up the entire free air volume between the sensor package and the borehole walls thereby preventing movement of the air in the vicinity of the sensor package. The validity of this hypothesis had been qualitatively proven by earlier experiments at ASL and by the sand installations at the IRIS/ASL stations ANMO in 1995 and COLA in 1996. This experiment documents the degree of improved noise levels to be expected if KS instruments are installed in sand instead of in the conventional manner.
Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits
Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.
Monitoring water content dynamics of biological soil crusts
Biological soil crusts (hereafter, “biocrusts”) dominate soil surfaces in nearly all dryland environments. To better understand the influence of water content on carbon (C) exchange, we assessed the ability of dual-probe heat-pulse (DPHP) sensors, installed vertically and angled, to measure changes in near-surface water content. Four DPHP sensors were installed in each of two research plots (eight sensors total) that differed by temperature treatment (control and heated). Responses were compared to horizontally installed water content measurements made with three frequency-domain reflectometry (FDR) sensors in each plot at 5-cm depth. The study was conducted near Moab, Utah, from April through September 2009. Results showed significant differences between sensor technologies: peak water content differences from the DPHP sensors were approximately three times higher than those from the FDR sensors; some of the differences can be explained by the targeted monitoring of biocrust material in the shorter DPHP sensor and by potential signal loss from horizontally installed FDR sensors, or by an oversampling of deeper soil. C-exchange estimates using the DPHP sensors showed a net C loss of 69 and 76 g C m −2 in control and heated plots, respectively. The study illustrates the potential for using the more sensitive data from shallow installations for estimating C exchange in biocrusts.
Applications of spectral band adjustment factors (SBAF) for cross-calibration
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 acquired from multiple spaceborne imaging sensors. However, an integrated global observation framework requires an understanding of how land surface processes are seen differently by various sensors. This is particularly true for sensors acquiring data in spectral bands whose relative spectral responses (RSRs) are not similar and thus may produce different results while observing the same target. The intrinsic offsets between two sensors caused by RSR mismatches can be compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of the two sensors. The motivation of this work comes from the need to compensate the spectral response differences of multispectral sensors in order to provide a more accurate cross-calibration between the sensors. In this paper, radiometric cross-calibration of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors was performed using near-simultaneous observations over the Libya 4 pseudoinvariant calibration site in the visible and near-infrared spectral range. The RSR differences of the analogous ETM+ and MODIS spectral bands provide the opportunity to explore, understand, quantify, and compensate for the measurement differences between these two sensors. The cross-calibration was initially performed by comparing the top-of-atmosphere (TOA) reflectances between the two sensors over their lifetimes. The average percent differences in the long-term trends ranged from $-$5% to $+$6%. The RSR compensated ETM+ TOA reflectance (ETM+$^{ast}$) measurements were then found to agree with MODIS TOA reflectance to within 5% for all bands when Earth Observing-1 Hy- erion hyperspectral data were used to produce the SBAFs. These differences were later reduced to within 1% for all bands (except band 2) by using Environmental Satellite Scanning Imaging Absorption Spectrometer for Atmospheric Cartography hyperspectral data to produce the SBAFs.
Optical techniques for the determination of nitrate in environmental waters: Guidelines for instrument selection, operation, deployment, maintenance, quality assurance, and data reporting
The recent commercial availability of in situ optical sensors, together with new techniques for data collection and analysis, provides the opportunity to monitor a wide range of water-quality constituents on time scales in which environmental conditions actually change. Of particular interest is the application of ultraviolet (UV) photometers for in situ determination of nitrate concentrations in rivers and streams. The variety of UV nitrate sensors currently available differ in several important ways related to instrument design that affect the accuracy of their nitrate concentration measurements in different types of natural waters. This report provides information about selection and use of UV nitrate sensors by the U.S. Geological Survey to facilitate the collection of high-quality data across studies, sites, and instrument types. For those in need of technical background and information about sensor selection, this report addresses the operating principles, key features and sensor design, sensor characterization techniques and typical interferences, and approaches for sensor deployment. For those needing information about maintaining sensor performance in the field, key sections in this report address maintenance and calibration protocols, quality-assurance techniques, and data formats and reporting. Although the focus of this report is UV nitrate sensors, many of the principles can be applied to other in situ optical sensors for water-quality studies.
A nationwide evaluation of crowd-sourced ambient temperature data
Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. The PurpleAir are the most widely-used low-cost air quality sensors in the contiguous United States with 14,777 deployed between 1 Jan 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. We compare temperature metrics reported by PurpleAir sensors with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor PurpleAir temperatures. We report associations of the difference between outdoor PurpleAir temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor PurpleAir sensors vis.a.vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that outdoor PurpleAir sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥ 21.1oC). Landcover and climate zone were significant modifiers of the association between PurpleAir and NLDAS-2 temperature metrics with the poorest PurpleAir- NLDAS-2 agreement in urban areas. Indoor PurpleAir temperatures lagged hourly NLDAS temperatures by two hours across almost all climate zones. The mean difference in hourly PurpleAir and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we find for use in epidemiological studies, that the PurpleAir sensor data is a reliable metric of daytime mean air temperature, but researchers should be aware of its limitations when examining extreme heat, or when aggregating sensor data across multiple years.
Water-level fluctuations, water temperatures, and tilts in sandbars -6.5R, 43.1L, and 172.3L, Grand Canyon, Arizona, 1990-93
Rill erosion, slumping, and fissuring develop on seepage faces of many sandbars along the Colorado River in the Grand Canyon at low river stage. Three sandbars were instrumented with sensors for continual monitoring of stage, pore pressure, ground-water temperature, and tilt to determine the relation between ground-water flow and sandbar deformation. Data were collected from October 1990 to July 1993 at sandbar -6.5R, which had 17 pore- pressure sensors, 1 stage sensor, 19 temperature sensors, and 8 tilt sensors. Data were collected from April 1991 to March 1993 at sandbar 172.3L, which had 15 pore-pressure sensors, 1 stage sensor, 29 temperature sensors, and 10 tilt sensors. Atten- uation of water-level fluctuation from the zone of fluctuating river stage to the back of the sandbars ranged from 70 percent at sandbar -6.5R to 40 percent for sandbars 43.1L and 172.3L. Shallow tilt occurred at sandbar 43.1L from July 7 to August 10, 1991. Tilt occurred at sandbar 172.3L on May 7-8, June 18-19, and September 1-2, 1991; July 3 and 31, 1992; January 12, 14, 20-21, and 31, 1993; and February 21 and 24, 1993.
The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset
Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 reflectance data showed that the between-sensor VI difference is below 4.5% for most of the indices. These results suggest that the harmonization is robust and the HLS V2.0 data are adequate for quantitative terrestrial applications.
Observing systems, modeling, and forecasting
Predicting harmful algal blooms (HABs) requires integrating physical, chemical, and biological data collected from observing networks and then assimilating these data into models, which are used to generate forecasts. In 2005, the Harmful Algal Research and Response: A National Environmental Science Strategy 2005-2015 (HARRNESS, 2005) made recommendations on how to improve HAB modeling and forecasting over the next decade. Key HARRNESS recommendations related to sensing, networking, and modeling HABs included: ● Support the development and validation of new and improved technologies for remote cell and toxin detection, and for modeling and forecasting, ● Improve coordination of monitoring/ and modeling efforts, both at national and regional levels, ● Improve the use of networking technologies for monitoring and modeling efforts, ● Conduct sustained time series measurements of the biotic, chemical, and physical environments impacted by HABs, ● Develop food web models on the ecosystem fate and effects of toxins, ● Develop and improve species-specific models that link to physical-biological models. Here we review HAB observing, modeling, and forecasting advances and technologies and recommend research and management priorities for the next decade and beyond. Our report encompasses sensing technologies, sensor networking and data management, models and forecasts, and the paths to operationalize forecasts. Continued improvements of deployable sensors are foundational to improving early warning indicators, models, and forecasts, which are only as good as the underlying data. Sensing technology has advanced considerably in the last decade; for example, more capable fluorometric pigment sensors can track changes in bloom biomass in real-time. Additionally, automated imaging/classifying systems to identify and quantify key harmful algal (HA) taxa are being routinely deployed. However, deployable toxin sensors are available for only some HAB toxins and continue to be identified as a critical need by researchers and managers. As more and improved sensors and technologies become available, the data quality associated with each sensor needs to be assessed. Data quality encompasses the reliability, accuracy, and uncertainty associated with sensor-generated data. These data need to be of known quality so that researchers, managers, and end-users can reliably determine if the information is appropriate for their intended applications. Many of the data quality recommendations from HARRNESS (2005) are still relevant and have been reiterated within the management community. Understanding and documenting data quality, and when applicable, standardizing best practices for sensor use, continue to be recommended.
Hurricane Rita surge data, southwestern Louisiana and southeastern Texas, September to November 2005
Pressure transducers and high-water marks were used to document the inland water levels related to storm surge generated by Hurricane Rita in southwestern Louisiana and southeastern Texas. On September 22-23, 2005, an experimental monitoring network consisting of 47 pressure transducers (sensors) was deployed at 33 sites over an area of about 4,000 square miles to record the timing, extent, and magnitude of inland hurricane storm surge and coastal flooding. Sensors were programmed to record date and time, temperature, and barometric or water pressure. Water pressure was corrected for changes in barometric pressure and salinity. Elevation surveys using global-positioning systems and differential levels were used to relate all storm-surge water-level data, reference marks, benchmarks, sensor measuring points, and high-water marks to the North American Vertical Datum of 1988 (NAVD 88). The resulting data indicated that storm-surge water levels over 14 feet above NAVD 88 occurred at three locations and rates of water-level rise greater than 5 feet per hour occurred at three locations near the Louisiana coast. Quality-assurance measures were used to assess the variability and accuracy of the water-level data recorded by the sensors. Water-level data from sensors were similar to data from co-located sensors, permanent U.S. Geological Survey streamgages, and water-surface elevations performed by field staff. Water-level data from sensors at selected locations were compared to corresponding high-water mark elevations. In general, the water-level data from sensors were similar to elevations of high quality high-water marks, while reporting consistently higher than elevations of lesser quality high-water marks.
Guidelines for calibration of uncrewed aircraft systems imagery
Executive Summary This report outlines quality assurance (QA) processes, including radiometric and geometric calibration guidelines, and guidelines for data acquisition and quality control to be followed by U.S. Geological Survey (USGS) researchers for acquiring and processing uncrewed aircraft systems (UAS) data. These QA processes ensure that UAS data can be used for quantitative analysis and are comparable with other standard geospatial data. Remote sensing data play a critical role in monitoring Earth’s resources. Traditionally, the USGS and Department of the Interior have used well calibrated metric sensors mounted on satellite or aircraft platforms to collect these data. These sensors and platforms are stable, and data have been processed using standard pipelines. These processes ensured that the data are generally consistent with each other and benefitted a diverse group of users. These data are shared among multiple researchers around the world through the internet and other means, using standard formats and metadata. In the last few years, UAS platforms have democratized the remote sensing data collection further, bringing an unparalleled level of control of time, sensors, and processes to individual researchers. Together with the development of cheaper and lighter sensors and relaxation of prohibitions against UAS operation in the National Airspace System by the Federal Aviation Authority, researchers can collect remote sensing data using UAS platforms. Researchers often customize the sensors on these UAS systems based on their specific requirements and use ad hoc processing steps to generate data. A challenge is that these data are often produced by a wide array of sensors and processes that render them potentially inconsistent with each other. Therefore, unlike data collected from metric sensors, UAS-based data are designed to benefit only specific user groups. The data thus generated often lack traceability to known standards, making them difficult to use with other geospatial data. This report provides radiometric and geometric calibration guidelines, as well as guidelines for data acquisition and quality control, that can be followed by USGS researchers in acquiring and processing UAS data. Instead of calibrating sensors, researchers collecting UAS data can focus on calibrating the data. Various radiometric calibration processes are provided, and the two panel empirical line method is highlighted for radiometric calibration. For geometric calibration, USGS and Department of the Interior researchers are experienced in following standard calibration procedures provided by standard UAS data processing software. However, researchers may be aided in paying attention to the tie points and the accuracy of the ground control points used for geometric calibration and data production. The accuracy of ground control points are related to the requirements of the project. The ground control points and the quality of tie points directly contribute to the geometric accuracy of the data, regardless of the ground sample distance of the imagery. The guidelines outlined in this report are intended to ensure that the data are in common units and are quantifiable and comparable with other data. These QA and calibration processes can be critical in ensuring that these datasets are used to the maximum extent possible. Including the calibration parameters (and their uncertainties) as part of metadata can allow for easier data discovery and analytical filters.
Field techniques for fluorescence measurements targeting dissolved organic matter, hydrocarbons, and wastewater in environmental waters: Principles and guidelines for instrument selection, operation and maintenance, quality assurance, and data reporting
The use of field deployable fluorescence sensors by the U.S. Geological Survey has become increasingly common for a wide variety of surface water and groundwater investigations. This report addresses field deployable fluorometers that measure the fluorescence response of various substances in water exposed to incident light generated by the sensor. An introduction to the basic principles of field measurements of fluorescence is provided, as well as technical background information on sensors that target dissolved organic matter, wastewater, and hydrocarbons, including sensor selection, operating principles, key features, and design elements. General deployment, operation and maintenance protocols, quality-assurance techniques, and suggestions for data reporting are presented to facilitate and standardize the collection and accurate communication of data collected by the U.S. Geological Survey across studies, sites, and sensor types. Sensor performance issues and common interferences are also described. An appendix is included to describe sensor calibration criteria and procedures, reporting units, and specific approaches to correct for interferences for fluorescence of dissolved organic matter sensors.
Observations and recommendations for coordinated calibration activities of government and commercial optical satellite systems
One of the biggest changes in the world of optical remote sensing over the last several years is the sheer increase in the number of sensors that are imaging the Earth in moderate to high spatial resolution. With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.
Ad hoc instrumentation methods in ecological studies produce highly biased temperature measurements
In light of global climate change, ecological studies increasingly address effects of temperature on organisms and ecosystems. To measure air temperature at biologically relevant scales in the field, ecologists often use small, portable temperature sensors. Sensors must be shielded from solar radiation to provide accurate temperature measurements, but our review of 18 years of ecological literature indicates that shielding practices vary across studies (when reported at all), and that ecologists often invent and construct ad hoc radiation shields without testing their efficacy. We performed two field experiments to examine the accuracy of temperature observations from three commonly used portable data loggers (HOBO Pro, HOBO Pendant, and iButton hygrochron) housed in manufactured Gill shields or ad hoc, custom‐fabricated shields constructed from everyday materials such as plastic cups. We installed this sensor array (five replicates of 11 sensor‐shield combinations) at weather stations located in open and forested sites. HOBO Pro sensors with Gill shields were the most accurate devices, with a mean absolute error of 0.2°C relative to weather stations at each site. Error in ad hoc shield treatments ranged from 0.8 to 3.0°C, with the largest errors at the open site. We then deployed one replicate of each sensor‐shield combination at five sites that varied in the amount of urban impervious surface cover, which presents a further shielding challenge. Bias in sensors paired with ad hoc shields increased by up to 0.7°C for every 10% increase in impervious surface. Our results indicate that, due to variable shielding practices, the ecological literature likely includes highly biased temperature data that cannot be compared directly across studies. If left unaddressed, these errors will hinder efforts to predict biological responses to climate change. We call for greater standardization in how temperature data are recorded in the field, handled in analyses, and reported in publications.
DUCKS: Low cost thermal monitoring units for near-vent deployment
During 1999 we designed and tested a thermal monitoring system to provide a cheap, robust, modular, real-time system capable of surviving the hostile conditions encountered proximal to active volcanic vents. In November 2000 the first system was deployed at Pu'u 'O'o (Kilauea, Hawai'i) to target persistently active vents. Aside from some minor problems, such as sensor damage due to tampering, this system remained operational until January 2004. The success of the prototype system led us to use the blueprint for a second installation at Stromboli (Aeolian Islands, Italy). This was deployed, dug into a bomb-proof bunker, during May 2002 and survived the April 2003 paroxysmal eruption despite being located just 250 m from the vent. In both cases, careful waterproofing of connectors and selection of suitable protection has prevented water damage and corrosion in the harsh atmosphere encountered at the crater rim. The Pu'u 'O'o system cost ∼US$10,000 and comprises four modules: sensors, transmission and power hub, repeater station and reception site. The sensor component consists of three thermal infrared thermometers housed in Pelican™ cases fitted with Germanium–Arsenide–Selenium windows. Two 1° field of view (FOV) sensors allow specific vents to be targeted and a 60° FOV sensor provides a crater floor overview. A hard wire connection links to a Pelican™-case-housed microprocessor , modem and power module. From here data are transmitted, via a repeater site, to a dedicated PC at the Hawaiian Volcano Observatory . Here data are displayed with a delay of ∼3 s between acquisition and display. The modular design allows for great flexibility. At Stromboli, 1° and 15° FOV sensor modules can be switched depending changes in activity style and crater geometry. In addition a direct line of site to the Stromboli reception center negates the repeater site requirement, reducing the cost to US$5500 for a single sensor system. We have also constructed self-contained units with internal data loggers for US$1500/unit. These have been tested at Kilauea, Stromboli, Etna, Masaya, Santiaguito, Fuego, Pacaya, Poas, Soufriere Hills, Villarrica and Erta Ale. These instruments have proved capable of detecting thermal signals associated with: (1) gas emission; (2) gas jetting events; (3) crater floor collapse; (4) lava effusion; (5) lava flow in tubes; (6) lava lake activity; (7) lava dome activity; and (8) crater lake skin temperature.