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USGS2004

Airborne Digital Sensor System and GPS-aided inertial technology for direct geopositioning in rough terrain

High-resolution airborne digital cameras with onboard data collection based on the Global Positioning System (GPS) and inertial navigation systems (INS) technology may offer a real-time means to gather accurate topographic map information by reducing ground control and eliminating aerial triangulation. Past evaluations of this integrated system over relatively flat terrain have proven successful. The author uses Emerge Digital Sensor System (DSS) combined with Applanix Corporation?s Position and Orientation Solutions for Direct Georeferencing to examine the positional mapping accuracy in rough terrain. The positional accuracy documented in this study did not meet large-scale mapping requirements owing to an apparent system mechanical failure. Nonetheless, the findings yield important information on a new approach for mapping in Antarctica and other remote or inaccessible areas of the world.

Open-File Report↗

USGS2015

Advanced Spaceborne Thermal Emission and Reflection Radiometer Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Product, algorithm theoretical basis document

This document provides an overview of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) supplemental algorithms in conjunction with the reuse of Landsat geometric algorithms modified by the National Aeronautics and Space Administration (NASA Land Processes Distributed Active Archive Center (LP DAAC) to create an ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) product. Implementation of these algorithms occurs within the AST_L1T product generation executable (PGE) as part of the open source Simple, Scalable, Script-based Science Processor for Missions (S4PM) processing software subsystem. The AST_L1T algorithms include the following: Generation of the AST_L1A input product via supplemental algorithms Application of the radiometric and geometric corrections appended in the AST_L1A product Application of cross-talk correction coefficients Generation and application of affine transformation coefficients Modification and reuse of Landsat’s geometric algorithms including: Systematic—generates the systematic grid Resampling—multi-use; only a single resample of input scene Gpyramid—scale input image to reference image (if necessary) Ground Control Point (GCP) Correlate—computes x,y offsets for GCPs to be used for precision grid generation Precision Refine—generates the precision grid GVERIFY—geometric verification

Open-File Report↗

USGS2021

System characterization of Earth observation sensors

This U.S. Geological Survey Open-File Report provides information from assessments of Earth observation sensors completed by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports are provided as independent measures of basic system performance by the Earth Resources Observation and Science Cal/Val Center of Excellence team by completing the geometric, radiometric, and spatial characterization. The results of these assessments are a snapshot in time.

Open-File Report↗

USGS2022

System characterization report on the Satellogic NewSat multispectral sensor

Executive Summary This report addresses system characterization of Satellogic’s NewSat satellite (also known as ÑuSat) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Since 2016, Satellogic has launched 17 NewSat satellites. All NewSat satellites have four-band imagers with a 1-meter (m) ground sample distance, and values in pixels are identical to values in meters. All NewSats have been launched into Sun-synchronous orbits of about 475 kilometers, with inclinations of about 97.5 degrees. The satellites have expected lifetimes of about 3 years. More information on the Satellogic satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://satellogic.com/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the NewSat satellites have an interior geometric performance in the range of −0.119 (−0.119 pixel) to 0.020 m (0.020 pixel) in easting and −0.148 (−0.148 pixel) to 0.014 m (0.014 pixel) in northing in band-to-band registration, an exterior geometric performance of −9.04 (−9.04 pixels) to −5.84 m (−5.84 pixels) in easting and 1.25 (1.25 pixels) to 3.11 m (3.11 pixels) in northing offset in comparison to Sentinel-2, an exterior geometric performance using ground control points of a 6.5-m circular error (95 percent), a radiometric performance in the range of 0.034 to 0.081 in offset and 0.652 to 0.808 in slope, and a spatial performance in the range of 1.61 to 1.76 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.081 to 0.138.

Open-File Report↗

USGS2011

Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades-An overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction，have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

International Journal of Environmental Research an↗

USGS2019

A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.

Geoscientific Model Development↗

USGS2024

Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies.

The Cryosphere↗

USGS2003

Testing laser-based sensors for continuous in situ monitoring of suspended sediment in the Colorado River, Arizona

High-resolution monitoring of sand mass balance in the Colorado River below Glen Canyon Dam, Arizona, USA, is needed for environmental management. In the Grand Canyon, frequent collection of suspended-sediment samples from cableways is logistically complicated, costly and provides limited spatial and temporal resolution. In situ laser sensors were tested in the Colorado River as an alternative method for monitoring the river's suspended transport. LISST data were collected at a fixed-depth, near-shore site while isokinetic measurements were simultaneously made from a nearby cableway. Diurnal variations in LISST grain size and concentration data compared well with depth-integrated, cross-section data. Tbe LISST was also successfully used to electronically trigger an ISCO 6712 pump sampler to provide continuous monitoring during periods when suspended concentrations exceeded the LISST's measurement range. Initial results indicate that the LISST can provide useful high-resolution suspended-sediment data within the Colorado River, when optics are maintained on a weekly basis.

Arizona↗

USGS2005

The detection and mapping of oil on a marshy area by a remote luminescent sensor

Airborne remote sensing can be a cost-effective method for monitoring pollutants in large areas such as occur in oil spills. An opportunity to test a particular method arose when a well ruptured and for 23 days spewed a 90-meter fountain of oil into the air, dispersing the oil over a wide area. The method tested was an airborne luminescence detector with a Fraunhofer Line Discriminator (FLD) which was flown over the affected area 41 days after the well was capped to obtain a map or the deposition pattern. To calibrate the system, samples of Spartina (wire grass) and Phragmites (common reed) were collected from the contaminated area and the oil residues were eluted in cyclohexane and quantitatively analyzed in a fluorescence photometer. Good correlation was observed between the remote sensor (FLD) and the laboratory analysis. Isopleths defining the deposition pattern of oil were drawn from the remote sensing information. A discussion will be presented on the feasibility of using this instrument for similar contamination incidents for cleanup and damage assessment.

Conference Paper↗

USGS1972

A land use classification scheme for use with remote sensor data

The needs of Federal agencies for a broad overview of national land use patterns, trends, and environmental impacts, with data inputs from both conventional sources and some of the more exotic sensors in high altitude aircraft and satellite platforms led to the formation in early 1971 of an Inter-Agency Steering Committee on Land Use Information and Classification. The work of this Committee, composed of representatives from the Geological Survey of the U.S. Department of the Interior, the Earth Observations Program of the National Aeronautics and Space Administration, the Soil Conservation Service of the U.S. Department of Agriculture, as well as the Association of American Geographers and the International Geographical Union, has been supported by NASA and the EROS Program of the Interior Department and coordinated by the USGS Geographic Applications Program. The Chairman of the Inter-Agency Committee was Dr. Arch C. Gerlach, Chief Geographer of the Geological Survey until his death in May 1972. Shortly before Dr. Gerlach's death, Dr. James R. Anderson was appointed Acting Chairman of the Committee.

Book↗

USGS2014

Optical sensors for water quality

Shifts in land use, population, and climate have altered hydrologic systems in the United States in ways that affect water quality and ecosystem function. Water diversions, detention in reservoirs, increased channelization, and changes in rainfall and snowmelt are major causes, but there are also more subtle causes such as changes in soil temperature, atmospheric deposition, and shifting vegetation patterns. The effects on water quality are complex and interconnected, and occur at timeframes of minutes (e.g., flash floods) to decades (e.g., evolving management practices). However, water-quality monitoring has historically focused on discrete samples collected weekly or monthly, and laboratory analyses that can take days or weeks to complete. Low-frequency data and delayed access hampers a timely response during events, limits the ability to identify specific causes or actions, and may result in poorly quantified effects on ecosystems and human health at local to regional scales. Recent advancements in commercially available in situ sensors, data platforms, and new techniques for data analysis provide an opportunity to monitor water quality in rivers, lakes, and estuaries on the time scales in which changes occur. For example, measurements that capture the variability in freshwater systems over time help to assess how shifts in seasonal runoff, changes in precipitation intensity, and increased frequencies of disturbances (such as fire and insect outbreaks) affect the storage, production, and transport of carbon and nitrogen in watersheds. Transmitting these data in real-time also provides information that can be used for early trend detection, help identify monitoring gaps, and provide sciencebased decision support across a range of issues related to water quality, freshwater ecosystems, and human health.

Lakeline↗

USGS2015

Testing the effectiveness of automated acoustic sensors for monitoring vocal activity of Marbled Murrelets Brachyramphus marmoratus

Cryptic nest sites and secretive breeding behavior make population estimates and monitoring of Marbled Murrelets Brachyramphus marmoratus difficult and expensive. Standard audio-visual and radar protocols have been refined but require intensive field time by trained personnel. We examined the detection range of automated sound recorders (Song Meters; Wildlife Acoustics Inc.) and the reliability of automated recognition models (“recognizers”) for identifying and quantifying Marbled Murrelet vocalizations during the 2011 and 2012 breeding seasons at Kodiak Island, Alaska. The detection range of murrelet calls by Song Meters was estimated to be 60 m. Recognizers detected 20 632 murrelet calls (keer and keheer) from a sample of 268 h of recordings, yielding 5 870 call series, which compared favorably with human scanning of spectrograms (on average detecting 95% of the number of call series identified by a human observer, but not necessarily the same call series). The false-negative rate (percentage of murrelet call series that the recognizers failed to detect) was 32%, mainly involving weak calls and short call series. False-positives (other sounds included by recognizers as murrelet calls) were primarily due to complex songs of other bird species, wind and rain. False-positives were lower in forest nesting habitat (48%) and highest in shrubby vegetation where calls of other birds were common (97%–99%). Acoustic recorders tracked spatial and seasonal trends in vocal activity, with higher call detections in high-quality forested habitat and during late July/early August. Automated acoustic monitoring of Marbled Murrelet calls could provide cost-effective, valuable information for assessing habitat use and temporal and spatial trends in nesting activity; reliability is dependent on careful placement of sensors to minimize false-positives and on prudent application of digital recognizers with visual checking of spectrograms.

Alaska↗

USGS2016

Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors

Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for measuring Essential Biodiversity Variables. Recent advances in camera technology and methods enable researchers to estimate changes in abundance and distribution for entire communities of animals and to identify global drivers of biodiversity trends. We suggest that interconnected networks of remote cameras will soon monitor biodiversity at a global scale, help answer pressing ecological questions, and guide conservation policy. This global network will require greater collaboration among remote-camera studies and citizen scientists, including standardized metadata, shared protocols, and security measures to protect records about sensitive species. With modest investment in infrastructure, and continued innovation, synthesis, and collaboration, we envision a global network of remote cameras that not only provides real-time biodiversity data but also serves to connect people with nature.

Frontiers in Ecology and the Environment↗

USGS1974

Measurement in a marine environment using low cost sensors of temperature and dissolved oxygen

Continuous records of physical parameters of the marine environment are difficult as well as expensive to obtain. This paper describes preliminary results of an investigative program with the purpose of developing low cost time integrating measurement and averaging devices for water temperature and dissolved oxygen. Measurements were made in an estuarine area of the Chesapeake Bay over two week periods. With chemical thermometers average water temperature for the two week period was found to be equal to average water temperature measured with thermocouples plus or minus 1.0 C. The slow diffusion of oxygen through the semipermiable sides of plastic bottles permitted the use of water filled bottles to obtain averaged oxygen measurements. Oxygen measurements for two week averaging times using 500 ml polyethylene bottles were found to vary from conventionally measured and averaged dissolved oxygen by about 1.8 mg/l.

Chesapeake Bay↗

USGS2025

Toward an efficient framework for remote sensing of river bathymetry: Comparing sensors and algorithms on an inaccessible proglacial river in Alaska

Remote sensing can provide reliable information on river depths and this approach might be particularly valuable in areas that are difficult to survey via conventional field methods. In this study, we assessed the potential to map the bathymetry of an inaccessible proglacial river in Alaska from both aerial orthophotos and a multispectral satellite image. In addition, we evaluated a variety of depth retrieval algorithms with different input data requirements, including some methods that require field measurements of water depth for calibration and other techniques that can be applied even when such field data are not available. These approaches might enable more efficient use of remote sensing methods by resource management agencies. Our results suggest that bathymetric mapping along the turquoise-colored river we examined was not only feasible but highly accurate ( R 2 up to 0.94) for both types of image data. Algorithms that use paired observations of depth and reflectance to train depth retrieval models were the most accurate, with errors on the order of 15%–20% and little or no bias. Alternative techniques based on hydraulic and statistical concepts also led to strong agreement between predicted and observed depths but were more susceptible to systematic biases toward under- or over-estimation of depth. In contrast to clear-flowing streams, bathymetric mapping in this environment was enabled by a direct relationship between the depth and brightness of the water due to scattering by suspended sediment. In selecting an appropriate depth retrieval method, a compromise might need to be reached between the level of field effort invested and the accuracy of the resulting image-derived bathymetry. Standalone software for implementing these techniques is freely available.

Alaska↗

USGS2012

A multi-sensor lidar, multi-spectral and multi-angular approach for mapping canopy height in boreal forest regions

Spatially explicit representations of vegetation canopy height over large regions are necessary for a wide variety of inventory, monitoring, and modeling activities. Although airborne lidar data has been successfully used to develop vegetation canopy height maps in many regions, for vast, sparsely populated regions such as the boreal forest biome, airborne lidar is not widely available. An alternative approach to canopy height mapping in areas where airborne lidar data is limited is to use spaceborne lidar measurements in combination with multi-angular and multi-spectral remote sensing data to produce comprehensive canopy height maps for the entire region. This study uses spaceborne lidar data from the Geosciences Laser Altimeter System (GLAS) as training data for regression tree models that incorporate multi-angular and multi-spectral data from the Multi-Angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging SpectroRadiometer (MODIS) to map vegetation canopy height across a 1,300,000 km 2 swath of boreal forest in Interior Alaska. Results are compared to in situ height measurements as well as airborne lidar data. Although many of the GLAS-derived canopy height estimates are inaccurate, applying a series of filters incorporating both data associated with the GLAS shots as well as ancillary data such as land cover can identify the majority of height estimates with significant errors, resulting in a filtered dataset with much higher accuracy. Results from the regression tree models indicate that late winter MISR imagery acquired under snow-covered conditions is effective for mapping canopy heights ranging from 5 to 15 m, which includes the vast majority of forests in the region. It appears that neither MISR nor MODIS imagery acquired during the growing season is effective for canopy height mapping, although including summer multi-spectral MODIS data along with winter MISR imagery does appear to provide a slight increase in the accuracy of resulting height maps. The finding that winter, snow-covered MISR imagery can be used to map canopy height is important because clear sky days are nearly three times as common during the late winter period as during the growing season. The increased odds of acquiring cloud-free imagery during the target acquisition period make regularly updated forest height inventories for Interior Alaska much more feasible. A major advantage of the GLAS–MISR–MODIS canopy height mapping methodology described here is that this approach uses only data that is freely available worldwide, making the approach potentially applicable across the entire circumpolar boreal forest region.

Remote Sensing of Environment↗

USGS2004

Seismographs, sensors, and satellites: Better technology for safer communities

In the past 25 years, our ability to measure, monitor, and model the processes that lead to natural disasters has increased dramatically. Equally important has been the improvement in our technological capability to communicate information about hazards to those whose lives may be affected. These innovations in tracking and communicating the changes-floods, earthquakes, wildfires, volcanic eruptions-in our dynamic planet, supported by a deeper understanding of earth processes, enable us to expand our predictive capabilities and point the way to a safer future. ?? 2004 Elsevier Ltd. All rights reserved.

Technology in Society↗

USGS2026

Comparative assessment of STIC sensors, streamflow and rain gauges for quantifying river connectivity in intermittent systems

In intermittent stream systems, including those occurring in Texas, USA, the severity of low-flow conditions, duration of seasonal disconnection, and frequency of no-flow events have been amplified by drought. Documentation of these no-flow events is necessary to evaluate ecosystem health. However, many intermittent reaches remain un-gauged given that perennial river sec-tions are often prioritized for gauge placement. Our objectives were to 1) document stream flow using Stream Temperature, Intermittency, and Conductivity (STIC) loggers to determine the frequency and duration of no-flow events in intermittent tributaries of the Colorado River, Texas and 2) compare logger data to publicly available data from streamflow discharge and precipitation gauge networks to understand differences among these data types for drying event characterization. We use these comparisons to summarize benefits and limitations of the application of in-stream data loggers. STIC loggers were deployed at 19 sites, one in each pool and riffle habitat of a stream reach. STIC loggers recorded a measurement of relative conductance every six hours from June 2022 to March 2024, which was used to determine the presence or absence of flow connectivity in a reach. No-flow duration among intermittent reaches varied between 37 and 270 days across tributaries during an ongoing drought in the study area. Overall, logger data was more precise than discharge data for characterizing no-flow events or precipitation data when documenting presence of water in the stream channel due to runoff. Lack of discharge gauges in intermittent tributaries left large sections of stream reaches undocumented and resulted in mischaracterization of flow patterns. Drought severity across the tributaries did not follow longitudinal patterns that would be expected by the climatic precipitation gradient of the study area. More research is needed to determine if factors such as population size affect severity. Likewise, precipitation data did not correlate well with logger water presence data, lacking consideration for groundwater recharge, soil hydrophobicity, and surface compaction. This study shows that to monitor no-flow events, detailed spatial datasets are necessary and that STIC loggers are useful tools that provide data to fill spatial information gaps and facilitate more accurate flow characterization and water presence data in intermittent systems.

Texas↗