Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Sensor Review”

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

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

At least 55 records · Page 3Linked to original sources
USGS2002

Evaluation of airborne thermal-infrared image data for monitoring aquatic habitats and cultural resources within the Grand Canyon

This study examined thermal-infrared (TIR) image data acquired using the airborne Advanced Thematic Mapper (ATM) sensor in the afternoon of July 25th, 2000 over a portion of the Colorado River corridor to determine the capability of these 100-cm resolution data to address some biologic and cultural resource requirements for GCMRC. The requirements investigated included the mapping of warm backwaters that may serve as fish habitats and the detection (and monitoring) of archaeological structures and natural springs that occur on land. This report reviews the procedure for calibration of the airborne TIR data to obtain surface water temperatures and shows the results for various river reaches within the acquired river corridor. With respect to mapping warm backwater areas, our results show that TIR data need to be acquired with a gain setting that optimizes the range of temperatures found within the water to increase sensitivity of the resulting data to a level of 0.1 °C and to reduce scan-line noise. Data acquired within a two-hour window around maximum solar heating (1:30 PM) is recommended to provide maximum solar heating of the water and to minimize cooling effects of late-afternoon shadows. Ground-truth data within the temperature range of the warm backwaters are necessary for calibration of the TIR data. The ground-truth data need to be collected with good locational accuracy. The derived water-temperature data provide the capability for rapid, wide-area mapping of warm-water fish habitats using a threshold temperature for such habitats. The collected daytime TIR data were ineffective in mapping (detecting) both archaeological structures and natural springs (seeps). The inability of the daytime TIR data to detect archaeological structures is attributed to the low thermal sensitivity (0.3 °C) of the collected data. The detection of subtle thermal differences between geologic materials requires sensitivities of at least 0.1 °C, which can be obtained by most TIR sensors using an appropriate gain setting. Simultaneous data collection for both land and water purposes can be achieved using sensors that collect TIR data in two separate channels, each channel using a gain setting most appropriate for land or water. The detection of archaeological structures and natural water seeps would also be improved by collection of data after sunset, which would require a separate data acquisition from that providing surface water temperature data and therefore additional cost. At this point, the cost for acquiring TIR data is quite high ($620/river-km) compared to the potential benefits of the data, unless reflected-wavelength data are also collected that can satisfy other GCMRC protocol requirements (such as mapping riparian vegetation). This is especially true if multiple data acquisitions are required during the year for temporal analyses of backwater areas. The cost for these data cannot be totally mitigated by its ability to partly replace the need for ground surveys of backwaters because calibration of the TIR data will require some ground-truth data from warm backwater areas (in addition to low-temperature main-stem data). However, the airborne data can provide a product that cannot be approached by ground surveys, that being an instantaneous (2 hour) map of surface water temperature over a 160-km stretch of the Grand Canyon.

Arizona↗
USGS2017

Tools for mapping ecosystem services

Mapping tools have evolved impressively in recent decades. From early computerised mapping techniques to current cloud-based mapping approaches, we have witnessed a technological evolution that has facilitated the democratisation of Geographic Information Systems (GIS). These advances have impacted multiple disciplines including ecosystem service (ES) mapping. The information that feeds different mapping tools is also increasingly accessible and complex. In this chapter, we review the evolution of mapping tools that are shaping the field of ES mapping together with the different sources of information that exist at this point. We discuss briefly the suitability of these approaches for mapping different ES types and for different scientific and policy aims. Finally, we elaborate on the integration of multiple tools (from desktop applications to sensor, web-based, or mobile devices) and on the future developments of these methods and the possibilities they may open for ES mapping.

Book chapter↗
USGS1988

Selected annotated bibliographies for adaptive filtering of digital image data

Digital spatial filtering is an important tool both for enhancing the information content of satellite image data and for implementing cosmetic effects which make the imagery more interpretable and appealing to the eye. Spatial filtering is a context-dependent operation that alters the gray level of a pixel by computing a weighted average formed from the gray level values of other pixels in the immediate vicinity. Traditional spatial filtering involves passing a particular filter or set of filters over an entire image. This assumes that the filter parameter values are appropriate for the entire image, which in turn is based on the assumption that the statistics of the image are constant over the image. However, the statistics of an image may vary widely over the image, requiring an adaptive or "smart" filter whose parameters change as a function of the local statistical properties of the image. Then a pixel would be averaged only with more typical members of the same population. This annotated bibliography cites some of the work done in the area of adaptive filtering. The methods usually fall into two categories, (a) those that segment the image into subregions, each assumed to have stationary statistics, and use a different filter on each subregion, and (b) those that use a two-dimensional "sliding window" to continuously estimate the filter either the spatial or frequency domain, or may utilize both domains. They may be used to deal with images degraded by space variant noise, to suppress undesirable local radiometric statistics while enforcing desirable (user-defined) statistics, to treat problems where space-variant point spread functions are involved, to segment images into regions of constant value for classification, or to "tune" images in order to remove (nonstationary) variations in illumination, noise, contrast, shadows, or haze. Since adpative filtering, like nonadaptive filtering, is used in image processing to accomplish various goals, this bibliography is organized in subsections based on application areas. Contrast enhancement, edge enhancement, noise suppression, and smoothing are typically performed in order imaging process, (for example, degradations due to the optics and electronics of the sensor, or to blurring caused by the intervening atmosphere, uniform motion, or defocused optics). Some of the papers listed may apply to more than one of the above categories; when this happens the paper is listed under the category for which the paper's emphasis is greatest. A list of survey articles is also supplied. These articles are general discussions on adaptive filters and reviews of work done. Finally, a short list of miscellaneous articles are listed which were felt to be sufficiently important to be included, but do not fit into any of the above categories. This bibliography, listing items published from 1970 through 1987, is extensive, but by no means complete. It is intended as a guide for scientists and image analysts, listing references for background information as well as areas of significant development in adaptive filtering.

Open-File Report↗
USGS2024

Concepts and evolution of urban hydrology.

Urbanization and climate change are exacerbating the flood risk and ecosystem degradation in urban catchments, with traditional stormwater management systems often overwhelmed. In this Review, we discuss changes in urban hydrology and approaches to stormwater management. Roughly 90% of rainfall on impervious surfaces and drainage infrastructure becomes run-off, enhancing rainfall export away from cities and leading to local water scarcity and downstream flooding and pollution. Projected increases in urban populations (68% in cities by 2050) and rainfall intensity (~12% in the 10-year and 50-year recurrence interval intensity, under 1.5 °C warming) will exacerbate these issues. Transforming stormwater systems is thus urgently needed, to mitigate flood risk and also to address community desires for environmental protection and enhanced water security. Opportunities include rain gardens and other nature-based stormwater control measures (which restore natural flows and offer other ecosystem services), smart sensor monitoring networks and real-time management (which sustain natural flow regimes, mitigate flood risk and protect ecosystem services) and stormwater harvesting (to avoid local water scarcity). Community acceptance of stormwater harvesting is as high as 96% and stormwater is a substantial resource, with volumes often exceeding demand in some parts of the world. Delivering additional transformations globally requires research into strategies to incentivize engagement and investment, and policies to guide governance of decentralized networks.

Nature Reviews Earth & Environment↗
USGS2019

Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities

Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.

Remote Sensing of Environment↗
USGS2004

Dust storms and their impact on ocean and human health: dust in Earth's atmosphere

Satellite imagery has greatly influenced our understanding of dust activity on a global scale. A number of different satellites such as NASA's Earth-Probe Total Ozone Mapping Spectrometer (TOMS) and Se-viewing Field-of-view Sensor (SeaWiFS) acquire daily global-scale data used to produce imagery for monitoring dust storm formation and movement. This global-scale imagery has documented the frequent transmission of dust storm-derived soils through Earth's atmosphere and the magnitude of many of these events. While various research projects have been undertaken to understand this normal planetary process, little has been done to address its impact on ocean and human health. This review will address the ability of dust storms to influence marine microbial population densities and transport of soil-associated toxins and pathogenic microorganisms to marine environments. The implications of dust on ocean and human health in this emerging scientific field will be discussed.

EcoHealth↗
USGS2019

Debris-flow monitoring and warning: Review and examples

Debris flows represent one of the most dangerous types of mass movements, because of their high velocities, large impact forces and long runout distances. This review describes the available debris-flow monitoring techniques and proposes recommendations to inform the design of future monitoring and warning/alarm systems. The selection and application of these techniques is highly dependent on site and hazard characterization, which is illustrated through detailed descriptions of nine monitoring sites: five in Europe, three in Asia and one in the USA. Most of these monitored catchments cover less than ∼10 km 2 and are topographically rugged with Melton Indices greater than 0.5. Hourly rainfall intensities between 5 and 15 mm/h are sufficient to trigger debris flows at many of the sites, and observed debris-flow volumes range from a few hundred up to almost one million cubic meters. The sensors found in these monitoring systems can be separated into two classes: a class measuring the initiation mechanisms, and another class measuring the flow dynamics. The first class principally includes rain gauges, but also contains of soil moisture and pore-water pressure sensors. The second class involves a large variety of sensors focusing on flow stage or ground vibrations and commonly includes video cameras to validate and aid in the data interpretation. Given the sporadic nature of debris flows, an essential characteristic of the monitoring systems is the differentiation between a continuous mode that samples at low frequency (“non-event mode”) and another mode that records the measurements at high frequency (“event mode”). The event detection algorithm, used to switch into the “event mode” depends on a threshold that is typically based on rainfall or ground vibration. Identifying the correct definition of these thresholds is a fundamental task not only for monitoring purposes, but also for the implementation of warning and alarm systems.

Earth-Science Reviews↗
USGS2009

Passive seismic monitoring of natural and induced earthquakes: Case studies, future directions and socio-economic relevance

An important discovery in crustal mechanics has been that the Earth’s crust is commonly stressed close to failure, even in tectonically quiet areas. As a result, small natural or man-made perturbations to the local stress field may trigger earthquakes. To understand these processes, Passive Seismic Monitoring (PSM) with seismometer arrays is a widely used technique that has been successfully applied to study seismicity at different magnitude levels ranging from acoustic emissions generated in the laboratory under controlled conditions, to seismicity induced by hydraulic stimulations in geological reservoirs, and up to great earthquakes occurring along plate boundaries. In all these environments the appropriate deployment of seismic sensors, i.e., directly on the rock sample, at the earth’s surface or in boreholes close to the seismic sources allows for the detection and location of brittle failure processes at sufficiently low magnitude-detection threshold and with adequate spatial resolution for further analysis. One principal aim is to develop an improved understanding of the physical processes occurring at the seismic source and their relationship to the host geologic environment. In this paper we review selected case studies and future directions of PSM efforts across a wide range of scales and environments. These include induced failure within small rock samples, hydrocarbon reservoirs, and natural seismicity at convergent and transform plate boundaries. Each example represents a milestone with regard to bridging the gap between laboratory-scale experiments under controlled boundary conditions and large-scale field studies. The common motivation for all studies is to refine the understanding of how earthquakes nucleate, how they proceed and how they interact in space and time. This is of special relevance at the larger end of the magnitude scale, i.e., for large devastating earthquakes due to their severe socio-economic impact.

Book chapter↗
USGS2020

Volcanological applications of unoccupied aircraft systems (UAS): Developments, strategies, and future challenges

Unoccupied aircraft systems (UAS) are developing into fundamental tools for tackling the grand challenges in volcanology; here, we review the systems used and their diverse applications. UAS can typically provide image and topographic data at two orders of magnitude better spatial resolution than space-based remote sensing, and close-range observations at temporal resolutions down to those of video frame rates. Responsive deployments facilitate dense time-series measurements, unique opportunities for geophysical surveys, sample collection from hostile environments such as volcanic plumes and crater lakes, and emergency deployment of ground-based sensors (and robots) into hazardous regions. UAS have already been used to support hazard management and decision-makers during eruptive crises. As technologies advance, increased system capabilities, autonomy and availability, supported by more diverse and lighter-weight sensors, will offer unparalleled potential for hazard monitoring. UAS are expected to provide opportunities for pivotal advances in our understanding of complex physical and chemical volcanic processes.

Volcanica↗
USGS2022

Evaluating the efficacy of aerial infrared surveys to detect artificial polar bear dens

The need to balance economic development with impacts to Arctic wildlife has been a prominent subject since petroleum exploration began on the North Slope of Alaska, USA, in the late 1950s. The North Slope region includes polar bears ( Ursus maritimus ) of the southern Beaufort Sea subpopulation, which has experienced a long-term decline in abundance. Pregnant polar bears dig dens in snow drifts during winter and are vulnerable to disturbance, as den abandonment and mortality of neonates may result. Maternal denning coincides with the peak season of petroleum exploration and construction, raising concerns that human activities may disrupt denning. To minimize disturbance of denning polar bears, aerial infrared (AIR) surveys are routinely used to search for dens within planned industry activity areas and that information is used to implement mitigation. Aerial infrared surveys target the heat signature emanating from dens. Despite use by industry for >15 years, the efficacy of AIR and the factors that impact its ability to detect dens remains uncertain. Here, we evaluate AIR using artificial dens and observers naïve to locations to estimate detection probability and its relationship with covariates including weather variables, den characteristics, infrared sensor and altitude, and survey order to identify potential evidence of in-flight observer learning occurring between surveys. In December 2019 we constructed 14 dens (each with an artificial heat source), and 11 control sites (disturbed sites without dens). Between December 2019 and January 2020, 3 survey crews flew 6 independent AIR surveys within the vicinity of dens and control sites and video-recorded AIR imagery. Observers identified putative dens either in flight or during post-flight review of recordings. We assessed detection probability with a simple Bayesian model using 3 subsets of data: 1) all detection/non-detection data; 2) detection/non-detection data restricted to instances where sample sites were confirmed to have been properly scanned by AIR during post-study verification (i.e., when den locations were known); and 3) all dens visible on the recorded imagery during post-study verification, even if they were not seen during the survey or during post-flight review. Subsets 1 and 2 most closely resembled den surveys flown for oil and gas industry and had detection probabilities of 0.15 (95% CI = 0.08–0.23) and 0.24 (95% CI = 0.13–0.37), respectively. Detection probability was 0.41 (95% CI = 0.25–0.58) for subset 3. Higher wind speeds and larger den volume negatively influenced detection probability. Our low detection rate compared to previous studies could partially be the result of differences in study design, such as survey flight patterns. Our results suggest that AIR, as it is currently used, is unlikely to detect most polar bear dens in surveyed areas. Resource managers who use AIR should consider a suite of additional methods (e.g., habitat mapping, probabilistic den distribution, AIR methodology improvements) for minimizing impacts of industry on denning polar bears.

Alaska↗
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↗
USGS2021

A review of timing accuracy across the Global Seismographic Network

The accuracy of timing across a seismic network is important for locating earthquakes as well as studies that use phase‐arrival information (e.g., tomography). The Global Seismographic Network (GSN) was designed with the goal of having reported timing be better than 10 ms. In this work, we provide a brief overview of how timing is kept across the GSN and discuss how clock‐quality metrics are embedded in Standard for Exchange of Earthquake Data records. Specifically, blockette 1001 contains the timing‐quality field, which can be used to identify time periods when poor clock quality could compromise timing accuracy. To verify the timing across the GSN, we compare cross‐correlation lags between collocated sensors from 1 January 2000 to 1 January 2020. We find that the mean error is less than 10 ms, with much of the difference likely coming from the method or uncertainty in the phase response of the instruments. This indicates that timing across the GSN is potentially better than 10 ms. We conclude that unless clock quality is compromised (as indicated in blockette 1001), GSN data’s timing accuracy should be suitable for most current seismological applications that require 10 ms accuracy. To assist users, the GSN network operators have implemented a “gsn_timing” metric available via the Incorporated Research Institutions for Seismology Data Management Center that helps users identify data with substandard timing accuracy (the 10 ms design goal of the GSN).

Seismological Research Letters↗
USGS2025

Object detection-assisted workflow facilitates cryptic snake monitoring

Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time-lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time-lapse photography require labor-intensive classification, limiting their use in large-scale studies. While many artificial intelligence-based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection-assisted hybrid workflow can greatly reduce time spent manually classifying data-heavy time-lapse snake studies and facilitate ecological monitoring for large snakes.

Florida↗
USGS2012

Detecting hidden volcanic explosions from Mt. Cleveland Volcano, Alaska with infrasound and ground-couples airwaves

In Alaska, where many active volcanoes exist without ground-based instrumentation, the use of techniques suitable for distant monitoring is pivotal. In this study we report regional-scale seismic and infrasound observations of volcanic activity at Mt. Cleveland between December 2011 and August 2012. During this period, twenty explosions were detected by infrasound sensors as far away as 1827 km from the active vent, and ground-coupled acoustic waves were recorded at seismic stations across the Aleutian Arc. Several events resulting from the explosive disruption of small lava domes within the summit crater were confirmed by analysis of satellite remote sensing data. However, many explosions eluded initial, automated, analyses of satellite data due to poor weather conditions. Infrasound and seismic monitoring provided effective means for detecting these hidden events. We present results from the implementation of automatic infrasound and seismo-acoustic eruption detection algorithms, and review the challenges of real-time volcano monitoring operations in remote regions. We also model acoustic propagation in the Northern Pacific, showing how tropospheric ducting effects allow infrasound to travel long distances across the Aleutian Arc. The successful results of our investigation provide motivation for expanded efforts in infrasound monitoring across the Aleutians and contributes to our knowledge of the number and style of vulcanian eruptions at Mt. Cleveland.

Alaska↗
USGS2000

Techniques for identifying dust devils in mars pathfinder images

Image processing methods used to identify and enhance dust devil features imaged by IMP (Imager for Mars Pathfinder) are reviewed. Spectral differences, visible red minus visible blue, were used for initial dust devil searches, driven by the observation that Martian dust has high red and low blue reflectance. The Martian sky proved to be more heavily dust-laden than pre-Pathfinder predictions, based on analysis of images from the Hubble Space Telescope. As a result, these initial spectral difference methods failed to contrast dust devils with background dust haze. Imager artifacts (dust motes on the camera lens, flat-field effects caused by imperfections in the CCD, and projection onto a flat sensor plane by a convex lens) further impeded the ability to resolve subtle dust devil features. Consequently, reference images containing sky with a minimal horizon were first subtracted from each spectral filter image to remove camera artifacts and reduce the background dust haze signal. Once the sky-flat preprocessing step was completed, the red-minus-blue spectral difference scheme was attempted again. Dust devils then were successfully identified as bright plumes. False-color ratios using calibrated IMP images were found useful for visualizing dust plumes, verifying initial discoveries as vortex-like features. Enhancement of monochromatic (especially blue filter) images revealed dust devils as silhouettes against brighter background sky. Experiments with principal components transformation identified dust devils in raw, uncalibrated IMP images and further showed relative movement of dust devils across the Martian surface. A variety of methods therefore served qualitative and quantitative goals for dust plume identification and analysis in an environment where such features are obscure.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2001

Transboundary movement of airborne pollutants — A methodology for integrating spaceborne images and ground based data

The availability of relevant and accurate environmental information is essential for environmental policy-makers. Recent improvements in satellite remote sensing technologies, groundbased monitors, and data access have resulted in the ability to observe and assess major atmospheric and ecological events around the world on a timely basis Each of these monitoring technologies reveals different and useful information, yet rarely are the resulting data sets used together in an integrated manner. The U.S. Environmental Protection Agency (EPA) and the United Nations Environment Programme (UNEP) Global Resource Information Database (GRID) office in Sioux Falls identified an environmental issue of global interest as a test case for applying an integrated approach: the transboundary movement of atmospheric pollutants. Transboundary movement of atmospheric pollutants has ramifications for human and environmental health, as well as economic impacts. As a result, it is the focus of many bilateral, regional, and international policy efforts. A central question with atmospheric pollutant transport is how to monitor pollutant movement and how to merge different monitoring datasets into useful information. Highly visible regional plumes of dust, smoke, and urban haze can be seen with satellite sensors, while ground-based monitoring of air pollutants such as fine particulates, SO2 , and toxics occurs at the local level. Integration of these two kinds of measurements allows the user to remotely observe large environmental effects in many areas of the world, while obtaining more detailed information from ground-based monitors. Hence, the combination of satellite-based sensor data and ground-based monitoring data promotes greater understanding of the movement of pollutants than either data set alone. Combined data sets are important for use by both scientists and international policy-makers. A standard methodology did not exist to guide and encourage integrated use of satellite images and ground-based data to monitor and understand major pollution events, such as air pollution. Thus, a small team was assembled to develop a methodology for the integration of satellite images and ground-based data. First, we conducted a literature and project review covering past and current integrated remote and ground-based data projects, a literature search of published work, and a search of data sets and technologies that could be used in a combined form. Second, based on this search and documentation, a general methodology was developed for using integrated spaceborne and ground-based data sets, intended as a guide for general scientists and policy-makers. Third, we found an existing project that was willing to be a pilot for testing the methodology: a U.S. EPANOAA project that was using aerial and ground-based sampling to learn more about the airborne sources of mercury deposition in the Florida Everglades. This document presents the results of the literature and project review, the complete methodology, and the outcome of the Florida Everglades pilot project.

Report↗
USGS1996

Total dissolved gas, barometric pressure, and water temperature data, lower Columbia River, Oregon and Washington, 1996

Increased levels of total dissolved gas pressure can cause gas-bubble trauma in fish downstream from dams on the Columbia River. In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey collected data on total dissolved gas pressure, barometric pressure, water temperature, and dissolved oxygen pressure at 11 stations on the lower Columbia River from the John Day forebay (river mile 215.6) to Wauna Mill (river mile 41.9) from March to September 1996. Methods of data collection, review, and processing are described in this report. Summaries of daily minimum, maximum, and mean hourly values are presented for total dissolved gas pressure, barometric pressure, and water temperature. Hourly values for these parameters are presented graphically. Dissolved oxygen data are not presented in this report because the quality-control data show that the data have poor precision and high bias. Suggested changes to monitoring procedures for future studies include (1) improved calibration procedures for total dissolved gas and dissolved oxygen to better define accuracy at elevated levels of supersaturation and (2) equipping dissolved oxygen sensors with stirrers because river velocities at the shoreline monitoring stations probably cannot maintain an adequate flow of water across the membrane surface of the dissolved oxygen sensor.

Oregon, Washington↗
USGS2013

Field calibration and validation of remote-sensing surveys

The Optical Collection Suite (OCS) is a ground-truth sampling system designed to perform in situ measurements that help calibrate and validate optical remote-sensing and swath-sonar surveys for mapping and monitoring coastal ecosystems and ocean planning. The OCS system enables researchers to collect underwater imagery with real-time feedback, measure the spectral response, and quantify the water clarity with simple and relatively inexpensive instruments that can be hand-deployed from a small vessel. This article reviews the design and performance of the system, based on operational and logistical considerations, as well as the data requirements to support a number of coastal science and management projects. The OCS system has been operational since 2009 and has been used in several ground-truth missions that overlapped with airborne lidar bathymetry (ALB), hyperspectral imagery (HSI), and swath-sonar bathymetric surveys in the Gulf of Maine, southwest Alaska, and the US Virgin Islands (USVI). Research projects that have used the system include a comparison of backscatter intensity derived from acoustic (multibeam/interferometric sonars) versus active optical (ALB) sensors, ALB bottom detection, and seafloor characterization using HSI and ALB.

Alaska↗