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At least 145 records · Page 8Linked to original sources

USGS1986

Evapotranspiration estimates using remote-sensing data, Parker and Palo Verde Valleys, Arizona and California

In 1981 the U.S. Geological Survey established an experimental project to assess the possible and practical use of remote sensing data to estimate evapotranspiration as an approximation of consumptive use in the lower Colorado River flood plain. The project area was in Parker Valley, Arizona. The approach selected was to measure the areas covered by each type of vegetation using remote sensing data in various types of analyses and to multiply each area by a predetermined water use rate. Two calibration and six remote sensing methods of classifying crop types were compared for cost, accuracy, consistency, and labor requirements. Included were one method each for field reconnaissance using 1982 data, low altitude (< than 5,000 ft) aerial photography using 1982 data, and visual photointerpretation of Landsat satellite images using 1981 and 1982 data; two methods for medium-altitude (15,000-18,000 ft) aerial photography using 1982 data; and three methods for digital Landsat satellite images using 1981 data. A test of the most promising digital processing method, which used three image dates, was made in part of Palo Verde Valley, California, where 1981 crop data were more complete than in Parker Valley. Of the eight methods studied, the three-date digital processing method was the most consistent and least labor-intensive; visual photointerpretation of Landsat images was the least expensive. Evapotranspiration estimates from crop classifications by all methods differed by a maximum of 6%. Total evapotranspiration calculated from crop data and phreatophyte maps in 1981 ranged from 12% lower in Palo Verde Valley to 17% lower in Parker Valley than consumptive use calculated by water budgets. The difference was greater in Parker Valley because the winter crop data were not included. (Author 's abstract)

Arizona, California↗

USGS2024

Remote sensing of volcano deformation and surface change

Volcanic unrest and eruptions are associated with surface deformation and landscape change that can be detected, characterized, and tracked via remote sensing measurements. Subsurface processes, including magma accumulation, withdrawal, and transport, can cause displacements at the surface that are best tracked at subaerial volcanoes with interferometric synthetic aperture radar (InSAR) and Global Navigation Satellite System (GNSS) measurements, although non-volcanic activity, like hydrothermal and tectonic sources, can complicate interpretations. Surface change is often associated with the emplacement of volcanic deposits, which modify the landscape and can experience post-emplacement deformation or morphological changes over time. Measurement of surface topography at volcanoes via remote means is a particularly important capability, given the control that topography exerts on many volcanic hazards and the potential for topographic change measurements to provide information about eruption rates. A much broader set of tools is available to investigate surface change at volcanoes, including not only InSAR and GNSS, but also synthetic aperture radar amplitude data, visible imagery, and lidar, acquired from airborne, ground-based, and satellite platforms. These data can also be used to identify instability of volcanic flanks and even have potential for use in detecting airborne ash plumes. Although hidden from traditional airborne and space-based remote sensing, deformation and surface change associated with submarine volcanism can be investigated with pressure sensors and bathymetric measurements—the below-water remote sensing analogs of GNSS and InSAR, respectively.

Book chapter↗

USGS2024

Joint Agency Commercial Imagery Evaluation (JACIE) best practices for remote sensing system evaluation and reporting

Executive Summary The Joint Agency Commercial Imagery Evaluation (JACIE) partnership consists of six agencies representing the U.S. Government’s commitment to promoting the use of high-quality remotely sensed data to meet scientific and other Federal needs. These agencies are large consumers of remotely sensed data and bring extensive experience in the assessment and use of these data. The six agencies are as follows: National Aeronautics and Space Administration, National Geospatial-Intelligence Agency, National Oceanic and Atmospheric Administration, U.S. Department of Agriculture, U.S. Geological Survey, and National Reconnaissance Office. JACIE was formed in 2001 to assess the quality of data from the nascent commercial high-resolution satellite industry. Since then, JACIE has expanded its purview to include data at various resolutions, including commercial and civil. The processes and techniques used by the JACIE agencies to assess data quality have been compiled within this report to share them across the agencies and with others who want to assess remotely sensed imagery data or understand how data are assessed and reported by JACIE.

Open-File Report↗

USGS1982

New papers related to remote sensing of the earth

Worldwide experience of many individuals and organizations with remote sensing data acquired from space, primarily Landsat-1, -2, and -3, has demonstrated its utility for land use classification, agricultural assessment, energy and resource exploration, and many other applications. There has been some reluctance to adopt these systems in operational programmes because of a lack of assured continuity of data collection, continuing changes in the systems, and some dissatisfaction with the types of data made available to users. Nevertheless, there is sufficient conviction of the value of satellite remote sensing so that new systems will be developed by several countries in the decade of the 1980's. The status changes continuously as studies are completed, and budgets are adjusted. This review presents the situation at the beginning of 1982.

Open-File Report↗

USGS2002

Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat . By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data , effective models of habitat preference were created. Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition. Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data . Such relationships, once verified and established, can be used to help indirectly monitor the population. Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat . Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations. The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

Arizona↗

USGS1989

Evapotranspiration estimates using remote-sensing data, Parker and Palo Verde valleys, Arizona and California

In 1981 the U.S. Geological Survey established an experimental project to assess the possible and practical use of remote-sensing data to estimate evapotranspiration as an approximation of consumptive use of water in the lower Colorado River flood plain. The project area was in Parker Valley, Arizona. The approach selected was to measure the areas covered by each type of vegetation, using remote-sensing data in various types of analyses, and to multiply each area by a predetermined water-use rate. Two calibration and six remote-sensing methods of classifying crop types were compared for cost, accuracy, consistency, and labor requirements. Included were one method each for field reconnaissance using 1982 data, low-altitude (less than 5,000 feet) aerial photography using 1982 data, and visual photointerpretation of Landsat satellite images using 1981 and 1982 data; two methods for medium-altitude (15,000-18,000 feet) aerial photography using 1982 data; and three methods for digital Landsat satellite images using 1981 data. A test of the most promising digital-processing method, which used three image dates, was made in part of Palo Verde Valley, California, where 1981 crop data were more complete than in Parker Valley. Of the eight methods studied, the two-date digital-processing method was the most consistent and least labor intensive for identifying two or three major crops; visual photointerpretation of Landsat images was the least expensive. Evapotranspiration estimates from crop classifications by all methods differed by a maximum of 6 percent. Total evapotranspiration calculated from crop data and phreatophyte maps in 1981 ranged from 11 percent lower in Palo Verde Valley to 17 percent lower in Parker Valley than consumptive use calculated by water budgets. The difference was greater in Parker Valley because the winter crop data were not included.

Water Supply Paper↗

USGS2021

Depths inferred from velocities estimated by remote sensing: A flow resistance equation-based approach to mapping multiple river attributes at the reach scale

Remote sensing of flow conditions in stream channels could facilitate hydrologic data collection, particularly in large, inaccessible rivers. Previous research has demonstrated the potential to estimate flow velocities in sediment-laden rivers via particle image velocimetry (PIV). In this study, we introduce a new framework for also obtaining bathymetric information: Depths Inferred from Velocities Estimated by Remote Sensing (DIVERS). This approach is based on a flow resistance equation and involves several assumptions: steady, uniform, one-dimensional flow and a direct proportionality between the velocity estimated at a given location and the local water depth, with no lateral transfer of mass or momentum. As an initial case study, we performed PIV and inferred depths from videos acquired from a helicopter hovering at multiple waypoints along a large river in central Alaska. The accuracy of PIV-derived velocities was assessed via comparison to field measurements and the performance of an optimization-based approach to DIVERS specification of roughness

Alaska↗

USGS1977

International training in remote sensing

The training and assistance programme at the Earth Resources Observation Systems (EROS) Data Center, Sioux Falls, South Dakota (United States of America), is designed to provide for the transfer of remote-sensing technology to land managers and resource specialists, both domestic and foreign. The programme does not emphasize research and development (i.e., sensor development, feasibility analysis, applications research etc.) nor does it provide services to operational remote-sensing applications projects. Instead, the objectives of the programme are to comprehend, interpret, assess and translate state-of-the-art remote-sensing techniques and transfer to users those techniques in such manner that they can be easily understood. The purpose of this paper is to describe briefly the user assistance programmes available at the EROS Data Center, with emphasis on the international activities.

Conference Paper↗

USGS1983

ROLES OF REMOTE SENSING AND CARTOGRAPHY IN THE USGS NATIONAL MAPPING DIVISION.

The inseparable roles of remote sensing and photogrammetry have been recognized to be consistent with the aims and interests of the American Society of Photogrammetry. In particular, spatial data storage, data merging and manipulation methods and other techniques originally developed for remote sensing applications also have applications for digital cartography. Also, with the introduction of much improved digital processing techniques, even relatively low resolution (80 m) traditional Landsat images can now be digitally mosaicked into excellent quality 1:250,000-scale image maps.

Conference Paper↗

USGS2012

State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill

The vast and persistent Deepwater Horizon (DWH) spill challenged response capabilities, which required accurate, quantitative oil assessment at synoptic and operational scales. Although experienced observers are a spill response's mainstay, few trained observers and confounding factors including weather, oil emulsification, and scene illumination geometry present challenges. DWH spill and impact monitoring was aided by extensive airborne and spaceborne passive and active remote sensing. Oil slick thickness and oil-to-water emulsion ratios are key spill response parameters for containment/cleanup and were derived quantitatively for thick (> 0.1 mm) slicks from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data using a spectral library approach based on the shape and depth of near infrared spectral absorption features. MODIS (Moderate Resolution Imaging Spectroradiometer) satellite, visible-spectrum broadband data of surface-slick modulation of sunglint reflection allowed extrapolation to the total slick. A multispectral expert system used a neural network approach to provide Rapid Response thickness class maps. Airborne and satellite synthetic aperture radar (SAR) provides synoptic data under all-sky conditions; however, SAR generally cannot discriminate thick (> 100 μm) oil slicks from thin sheens (to 0.1 μm). The UAVSAR's (Uninhabited Aerial Vehicle SAR) significantly greater signal-to-noise ratio and finer spatial resolution allowed successful pattern discrimination related to a combination of oil slick thickness, fractional surface coverage, and emulsification. In situ burning and smoke plumes were studied with AVIRIS and corroborated spaceborne CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation) observations of combustion aerosols. CALIPSO and bathymetry lidar data documented shallow subsurface oil, although ancillary data were required for confirmation. Airborne hyperspectral, thermal infrared data have nighttime and overcast collection advantages and were collected as well as MODIS thermal data. However, interpretation challenges and a lack of Rapid Response Products prevented significant use. Rapid Response Products were key to response utilization—data needs are time critical; thus, a high technological readiness level is critical to operational use of remote sensing products. DWH's experience demonstrated that development and operationalization of new spill response remote sensing tools must precede the next major oil spill.

Remote Sensing of Environment↗

USGS1994

Extraction of spatial information from remotely sensed image data - an example: gloria sidescan sonar images

A method to extract spatial amplitude and variability information from remotely sensed digital imaging data is presented. High Pass Filters (HPFs) are used to compute both a Spatial Amplitude Image/Index (SAI) and Spatial Variability Image/Index (SVI) at the local, intermediate, and regional scales. Used as input to principal component analysis and automatic clustering classification, the results indicate that spatial information at scales other than local is useful in the analysis of remotely sensed data. The resultant multi-spatial data set allows the user to study and analyze an image based more on the complete spatial characteristics of an image than only local textural information.

Canadian Journal of Remote Sensing↗

USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗

USGS2026

Remote sensing enables basin-scale inventories of coal mine methane

Underground coal mines are important global sources of methane, but emission estimates are uncertain. We show that emission estimates for individual mines from aircraft remote-sensing surveys in the United States agree within 40% with direct measurements used for national emission reporting (IPCC Tier 3 estimate). Such direct measurements are unavailable in most countries, which rely on estimated emission factors (EFs) applied to coal-production rates. We find that EFs from IPCC Tier 1 and the Model for Calculating Coal Mine Methane (MC2M) methods overestimate U.S. emissions 3-fold due to incorrect dependence on mine depth. An IPCC Tier 2 method using measured basin-specific mine gas content agrees with direct emission measurements but does not account for gob well emissions and requires gas content data that are generally unavailable. We show that aircraft remote sensing for a small sample of mines can successfully estimate basin-specific EFs for ventilation shafts and gob wells, enabling estimates of basin- and national-scale emissions. We discuss how the method can be applied with satellite remote sensing to quantify coal emissions worldwide.

Alabama, Colorado, Kentucky, New Mexico, Ohio, Pen↗

USGS2019

An experimental evaluation of the feasibility of inferring concentrations of a visible tracer dye from remotely sensed data in turbid rivers

The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an Optimal Band Ratio Analysis (OBRA) algorithm to these data sets indicated strong relationships between reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40-60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from a sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this approach can be extended to more turbid rivers than those examined previously.

Missouri↗

USGS2016

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk ( Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Journal of Visualized Experiments↗

USGS2016

Estimating forest and woodland aboveground biomass using active and passive remote sensing

Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R 2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14 Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1 . Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.

Photogrammetric Engineering and Remote Sensing↗

USGS2005

Estimating discharge in rivers using remotely sensed hydraulic information

A methodology to estimate in-bank river discharge exclusively from remotely sensed hydraulic data is developed. Water-surface width and maximum channel width measured from 26 aerial and digital orthophotos of 17 single channel rivers and 41 SAR images of three braided rivers were coupled with channel slope data obtained from topographic maps to estimate the discharge. The standard error of the discharge estimates were within a factor of 1.5-2 (50-100%) of the observed, with the mean estimate accuracy within 10%. This level of accuracy was achieved using calibration functions developed from observed discharge. The calibration functions use reach specific geomorphic variables, the maximum channel width and the channel slope, to predict a correction factor. The calibration functions are related to channel type. Surface velocity and width information, obtained from a single C-band image obtained by the Jet Propulsion Laboratory's (JPL's) AirSAR was also used to estimate discharge for a reach of the Missouri River. Without using a calibration function, the estimate accuracy was +72% of the observed discharge, which is within the expected range of uncertainty for the method. However, using the observed velocity to calibrate the initial estimate improved the estimate accuracy to within +10% of the observed. Remotely sensed discharge estimates with accuracies reported in this paper could be useful for regional or continental scale hydrologic studies, or in regions where ground-based data is lacking. ?? 2004 Elsevier B.V. All rights reserved.

Journal of Hydrology↗

USGS2009

Foreword to the issue on remote sensing of regional land use and land cover

This issue showcases some good work from Chinese authors who attended the International Workshop of Earth Observation and Remote Sensing Applications (EORSA), held in Beijing, China, June 30-July 2, 2008. The five articles in this issue focus on remote sensing of regional land use and land cover.

IEEE Journal of Selected Topics in Applied Earth O↗