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At least 55 records · Page 3Linked to original sources
USGS1994

Airborne remote sensing for geology and the environment; present and future

In 1988, a group of leading experts from government, academia, and industry attended a workshop on airborne remote sensing sponsored by the U.S. Geological Survey (USGS) and hosted by the Branch of Geophysics. The purpose of the workshop was to examine the scientific rationale for airborne remote sensing in support of government earth science in the next decade. This report has arranged the six resulting working-group reports under two main headings: (1) Geologic Remote Sensing, for the reports on geologic mapping, mineral resources, and fossil fuels and geothermal resources; and (2) Environmental Remote Sensing, for the reports on environmental geology, geologic hazards, and water resources. The intent of the workshop was to provide an evaluation of demonstrated capabilities, their direct extensions, and possible future applications, and this was the organizational format used for the geologic remote sensing reports. The working groups in environmental remote sensing chose to present their reports in a somewhat modified version of this format. A final section examines future advances and limitations in the field. There is a large, complex, and often bewildering array of remote sensing data available. Early remote sensing studies were based on data collected from airborne platforms. Much of that technology was later extended to satellites. The original 80-m-resolution Landsat Multispectral Scanner System (MSS) has now been largely superseded by the 30-m-resolution Thematic Mapper (TM) system that has additional spectral channels. The French satellite SPOT provides higher spatial resolution for channels equivalent to MSS. Low-resolution (1 km) data are available from the National Oceanographic and Atmospheric Administration's AVHRR system, which acquires reflectance and day and night thermal data daily. Several experimental satellites have acquired limited data, and there are extensive plans for future satellites including those of Japan (JERS), Europe (ESA), Canada (Radarsat), and the United States (EOS). There are currently two national airborne remote sensing programs (photography, radar) with data archived at the USGS' EROS Data Center. Airborne broadband multispectral data (comparable to Landsat MSS and TM but involving several more channels) for limited geographic areas also are available for digital processing and analysis. Narrow-band imaging spectrometer data are available for some NASA experiment sites and can be acquired for other locations commercially. Remote sensing data and derivative images, because of the uniform spatial coverage, availability at different resolutions, and digital format, are becoming important data sets for geographic information system (GIS) analyses. Examples range from overlaying digitized geologic maps on remote sensing images and draping these over topography, to maps of mineral distribution and inferred abundance. A large variety of remote sensing data sets are available, with costs ranging from a few dollars per square mile for satellite digital data to a few hundred dollars per square mile for airborne imaging spectrometry. Computer processing and analysis costs routinely surpass these expenses because of the equipment and expertise necessary for information extraction and interpretation. Effective use requires both an understanding of the current methodology and an appreciation of the most cost-effective solution.

Bulletin↗
USGS2005

Leaf optical property changes associated with the occurrence of Spartina alterniflora dieback in Coastal Louisiana related to remote sensing mapping

In order to provide a remote sensing solution that would detect both the initial onset and monitor the early, as well as, the later stages of impact progression, changes in live leaf optical properties were compared along transects spanning impacted coastal Louisiana marsh sites. Green and red edge reflectance trends generally represented the early stages and fairly well the later stages of dieback progression, while blue and red reflectance and absorption trends represented the later stages of marsh impact that were most closely related to visible signs of marsh impact. Leaf reflectance in the near infrared (NIR) was not compatible with visual reflectance trends and did not co-vary with derived indicators of leaf water content, and thereby, water stress. Predicted from reflectance ratios, carotene tended to remain constant or increase relative to chlorophyll following noted changes in stressed plants at the two least impacted sites, while the pigments co-varied at the two most impacted sites. As an operational solution most amenable for satellite remote sensing, the NIR /red ratio followed blue and red reflectance trends while the NIR /green ratio mimicked the green and red edge reflectance trends indicating impact onset and progression, as well as, generally portraying blue and red reflectance trends indicating later stages of impact. The NIR/ green ratio magnitude and range generally increased from the most to least impacted site providing a convenient method to detect dieback onset and monitor dieback progression. This research demonstrated that remote sensing mapping at these sites could offer a more accurate perception of dieback severity distribution than offered by determinations relying on visible indicators of marsh changes.

Louisiana↗
USGS2014

Remote sensing of land surface phenology

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php .

Fact Sheet↗
USGS1981

Remote sensing: a tool for park planning and management

Remote sensing may be defined as the science of imaging or measuring objects from a distance. More commonly, however, the term is used in reference to the acquisition and use of photographs, photo-like images, and other data acquired from aircraft and satellites. Thus, remote sensing includes the use of such diverse materials as photographs taken by hand from a light aircraft, conventional aerial photographs obtained with a precision mapping camera, satellite images acquired with sophisticated scanning devices, radar images, and magnetic and gravimetric data that may not even be in image form. Remotely sensed images may be color or black and white, can vary in scale from those that cover only a few hectares of the earth's surface to those that cover tens of thousands of square kilometers, and they may be interpreted visually or with the assistance of computer systems. This article attempts to describe several of the commonly available types of remotely sensed data, to discuss approaches to data analysis, and to demonstrate (with image examples) typical applications that might interest managers of parks and natural areas.

Parks↗
USGS2025

Assessing the potential for evaluation of wildland fire models using remotely sensed data—Summary proceedings from a U.S. Geological Survey workshop in 2024

On September 19, 2024, the U.S. Geological Survey (USGS) held a virtual workshop titled “Potential for Evaluation of Fire Models with Remote Sensing Data Workshop” to assess the feasibility of using remotely sensed datasets to evaluate next-generation wildland fire behavior models. Remote sensing and fire modelling experts gathered to: (1) assess the suitability of a variety of classified, commercial, and publicly available remotely sensed datasets for advancing fire model evaluation; (2) develop ideas on how to integrate remotely sensed data products with fire model inputs and outputs; and (3) identify any barriers and limitations to performing an evaluation of next-generation fire models. The USGS National Civil Applications Center, USGS Earth Resources Observation and Science Center, and USGS Fort Collins Ecosystem Science Center presented information on remote sensing datasets for three Arizona wildfire case studies. The development teams of the Fire Dynamics Simulator and QUIC-Fire fire behavior models presented their models and current evaluation methodologies. Interspersed with these presentations were discussions regarding how to expand current wildfire remote sensing data collection efforts beyond operational needs to assist in future fire modeling. Workshop participants agreed that several of the remote sensing datasets have potential for wildfire model evaluation. However, participants also identified several barriers and complications to performing a model evaluation including key gaps in wildfire datasets; uncertainties related to model fire-atmosphere reinitiation; lack of ground truthing and atmospheric correction of remotely sensed datasets; and differences in spatial, geolocation, radiometric, and temporal resolutions between the datasets and models. Further, the absence of standardized methodologies for image interpretation, poor understanding of sensor capabilities and limitations, and a lack of automation also hinder model evaluation efforts. Based on feedback from this workshop, USGS fire modelers are considering a project to address the uncertainties related to fire model reinitiation and encouraging fire practitioners to collaborate with remote sensing experts on wildland fires to improve data collection for a broader community of practice. Additionally, multiagency efforts are in development for a comprehensive cross-sensor validation and ground-truth campaign to test spatial, spectral, and geolocation sensor capabilities, determine limitations, and identify observational gaps for future sensor development and acquisition.

Arizona↗
USGS1990

Instantaneous and daily values of the surface energy balance over agricultural fields using remote sensing and a reference field in an arid environment

Remotely sensed surface temperature and reflectance in the visible and near infrared wavebands along with ancilliary meteorological data provide the capability of computing three of the four surface energy balance components (i.e., net radiation, soil heat flux, and sensible heat flux) at different spatial and temporal scales. As a result, under nonadvective conditions, this enables the estimation of the remaining term (i.e., the latent heat flux). One of the practical applications with this approach is to produce evapotranspiration (ET) maps for agricultural regions which consist of an array of fields containing different crops at varying stages of growth and soil moisture conditions. Such a situation exists in the semiarid southwest at the University of Arizona Maricopa Agricultural Center, south of Phoenix. For one day (14 June 1987), surface temperature and reflectance measurements from an aircraft 150 m above ground level (agl) were acquired over fields from zero to nearly full cover at four times between 1000 MST and 1130 MST. The diurnal pattern of the surface energy balance was measured over four fields, which included alfalfa at 60% cover, furrowed cotton at 20% and 30% cover, and partially plowed what stubble. Instantaneous and daily values of ET were estimated for a representative area around each flux site with an energy balance model that relies on a reference ET. This reference value was determined with remotely sensed data and several meteorological inputs. The reference ET was adjusted to account for the different surface conditions in the other fields using only remotely sensed variables. A comparison with the flux measurements suggests the model has difficulties with partial canopy conditions, especially related to the estimation of the sensible heat flux. The resulting errors for instantaneous ET were on the order of 100 W m −2 and for daily values of order 2 mm day −1 . These findings suggest future research should involve development of methods to account for the variability of meteorological parameters brought about by changes in surface conditions and improvements in the modeling of sensible heat transfer across the surface—atmosphere interface for partial canopy conditions using remote sensing information.

Remote Sensing of Environment↗
USGS2005

Commerical Remote Sensing Data Contract

The U. S. Geological Survey's (USGS) Commercial Remote Sensing Data Contracts (CRSDCs) provide government agencies with access to a broad range of commercially available remotely sensed airborne and satellite data. These contracts were established to support The National Map partners, other Federal Civilian agency programs, and Department of Defense programs that require data for the United States and its territories. Experience shows that centralized procurement of remotely sensed data leads to considerable cost savings to the Federal government through volume discounts, reduction of redundant contract administrative costs, and avoidance of duplicate purchases. These contracts directly support the President's Commercial Remote Sensing Space Policy, signed in 2003, by providing a centralized mechanism for civil agencies to acquire commercial remote sensing products to support their mission needs in an efficient and coordinated way. CRSDC administration is provided by the USGS Mid-Continent Mapping Center in Rolla, Missouri.

Fact Sheet↗
USGS2010

Surface energy balance estimates at local and regional scales using optical remote sensing from an aircraft platform and atmospheric data collected over semiarid rangelands

Remotely sensed data in the visible, near-infrared, and thermal-infrared wave bands were collected from a low-flying aircraft during the Monsoon '90 field experiment. Monsoon '90 was a multidisciplinary experiment conducted in a semiarid watershed. It had as one of its objectives the quantification of hydrometeorological fluxes during the “monsoon” or wet season. The remote sensing observations along with micrometeprological and atmospheric boundary layer (ABL) data were used to compute the surface energy balance over a range of spatial scales. The procedure involved averaging multiple pixels along transects flown over the meteorological and flux (METFLUX) stations. Average values of the spectral reflectance and thermal-infrared temperatures were computed for pixels of order 10 −1 to 10 1 km in length and were used with atmospheric data for evaluating net radiation ( R n ), soil heat flux ( G ), and sensible ( H ) and latent ( LE ) heat fluxes at these same length scales. The model employs a single-layer resistance approach for estimating H that requires wind speed and air temperature in the ABL and a remotely sensed surface temperature. The values of R n and G are estimated from remote sensing information together with near-surface observations of air temperature, relative humidity, and solar radiation. Finally, LE is solved as the residual term in the surface energy balance equation. Model calculations were compared to measurements from the METFLUX network for three days having different environmental conditions. Average percent differences for the three days between model and the METFLUX estimates of the local fluxes were about 5% for R n , 20% for G and H , and 15% for LE . Larger differences occurred during partly cloudy conditions because of errors in interpreting the remote sensing data and the higher spatial and temporal variation in the energy fluxes. Minor variations in modeled energy fluxes were observed when the pixel size representing the remote sensing inputs changed from 0.2 to 2 km. Regional scale estimates of the surface energy balance using bulk ABL properties for the model parameters and input variables and the 10-km pixel data differed from the METFLUX network averages by about 4% for R n , 10% for G and H , and 15% for LE . Model sensitivity in calculating the turbulent fluxes H and LE to possible variations in key model parameters (i.e., the roughness lengths for heat and momentum) was found to be fairly significant. Therefore the reliability of the methods for estimating key model parameters and potential errors needs further testing over different ecosystems and environmental conditions.

Water Resources Research↗
USGS2003

The U.S. Geological Survey Land Remote Sensing Program

In 2002, the U. S. Geological Survey (USGS) launched a program to enhance the acquisition, preservation, and use of remotely sensed data for USGS science programs, as well as for those of cooperators and customers. Remotely sensed data are fundamental tools for studying the Earth's land surface, including coastal and near-shore environments. For many decades, the USGS has been a leader in providing remotely sensed data to the national and international communities. Acting on its historical topographic mapping mission, the USGS has archived and distributed aerial photographs of the United States for more than half a century. Since 1972, the USGS has acquired, processed, archived, and distributed Landsat and other satellite and airborne remotely sensed data products to users worldwide. Today, the USGS operates and manages the Landsats 5 and 7 missions and cooperates with the National Aeronautics and Space Administration (NASA) to define and implement future satellite missions that will continue and expand the collection of moderate-resolution remotely sensed data. In addition to being a provider of remotely sensed data, the USGS is a user of these data and related remote sensing technology. These data are used in natural resource evaluations for energy and minerals, coastal environmental surveys, assessments of natural hazards (earthquakes, volcanoes, and landslides), biological surveys and investigations, water resources status and trends analyses and studies, and geographic and cartographic applications, such as wildfire detection and tracking and as a source of information for The National Map. The program furthers these distinct but related roles by leading the USGS activities in providing remotely sensed data while advancing applications of such data for USGS programs and a wider user community.

Fact Sheet↗
USGS1978

A perspective on remote sensing for wildlife management

The Pecora IV Symposium (Applications of Remote Sensing Data to Wildlife Management) conducted October 10-12, 1978, in Sioux Falls, S.D.; provided a perspective on the uses of remote sensing techniques for wildlife management. The task of summarizing the Symposium, which is the objective of this paper, is not simple because of the diversity of opinions presented regarding (a) the magnitude of the wildlife management problems, and (b) the uses of remotely sensed data to address these problems. Factors which contributed to the diversity of opinions presented include: varying degrees of experience with wildlife management problems, differences in experience with remote sensing applications, and level of responsibility within the management decision-making process.

Conference Paper↗
USGS2024

A global view of remote sensing of rangelands: Evolution, applications, future pathways

The application of digital remote sensing to rangelands is as long as the history of digital remote sensing itself. Before the launch of the Earth Resources Technology Satellite (ERTS) – later renamed Landsat, scientists were evaluating the use of multispectral aerial imagery to map soils and range vegetation (Yost and Wenderoth 1969). During the late 1960’s, the promise of ERTS, designed to drastically improve our ability to update maps and study earth resources, particularly in developing countries, was eagerly anticipated by a number of government agencies (Carter 1969). With the ERTS launch on July 23, 1972, a flurry of research activity aimed at the application of this new data source to map earth resources began. Practitioners who pioneered the use of satellite based digital remote sensing found the new data source a significant value for rangeland assessments (e.g., Rouse et al., 1973, Rouse et al., 1974, Bauer 1976). This early work established many of the basic techniques still in use today to assess and monitor global rangelands. The following sub-sections discuss the evolution of remote sensing data, methods, and approaches in various decades.

Book chapter↗
USGS2026

Bridging remote sensing advances and management needs for small Prairie Pothole waterbodies using a multiscale accuracy assessment

Remote sensing of surface water provides a powerful tool to inform the management of waterfowl habitat, but there is little information available to directly assess the relative accuracy of different remote sensing datasets. Our objective was to understand how the characteristics of remotely sensed inundation datasets inform dataset accuracy, the reliable detection of small waterbodies, the distribution of surface water, observation frequency and completeness of data: all attributes relevant to the management of waterfowl habitat. We compared surface water composites from 10 remotely sensed surface water datasets from 2016 to 2021 to in-situ surface water data for three complexes of small waterbodies in the U.S. Prairie Pothole Region and evaluated their accuracy at the pixel, waterbody and local landscape scales. While all products had high per-pixel balanced accuracies (>0.75), we found distinct differences in waterbody area and landscape distribution estimates among datasets. Sentinel-1-based datasets provided a more complete set of observations over time and were more sensitive in detecting water presence in smaller waterbodies but were less accurate at identifying waterbody area than other datasets evaluated. Landsat datasets, alternatively, produced simpler landscape distributions that largely omitted the smallest waterbodies. While the datasets that either fused Sentinel-1 and −2 data collections or utilized local training data had the highest performances (e.g. balanced accuracy = 0.92), all datasets had use-case scenarios for which they may be informative. Our comparisons revealed differences that were not evident in traditional pixel-scale accuracy assessment, such as an 18-fold difference in the number of inundated waterbodies identified across remote sensing datasets. These findings provide novel insights for waterfowl conservation management on howremote sensing datasets may differ in their ability to monitor annual spring surface water presence within landscapes dominated by small waterbodies, such as the Prairie Pothole Region.

Minnesota, North Dakota↗
USGS2012

Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications

Several methods for the analysis of remotely sensed reflectance data are compared, including empirical methods and scattering theories, both of which are important for solving remote sensing problems. The concept of the photon mean optical path length and the implications for use in modeling reflectance spectra are presented. It is shown that the mean optical path length in a particulate surface is in rough inverse proportion to the square root of the absorption coefficient. Thus, the stronger absorber a material is, the less photons will penetrate into the surface. The concept of apparent absorbance (‐In reflectance) is presented, and it is shown that absorption bands, which are Gaussian in shape when plotted as absorption coefficient (true absorbance) versus photon energy, are also Gaussians in apparent absorbance. However, the Gaussians in apparent absorbance have a smaller intensity and a width which is a factor of √2 larger. An apparent continuum in a reflectance spectrum is modeled as a mathematical function used to isolate a particular absorption feature for analysis. It is shown that a continuum should be removed by dividing it into the reflectance spectrum or subtracting it from the apparent absorbance and that the fitting of Gaussians to absorption features should be done using apparent absorbance versus photon energy. Kubelka‐Munk theory is only valid for materials with small total absorption and for bihemispherical reflectance, which are rarely encountered in geologic remote sensing. It is shown that the recently advocated bidirectional reflectance theories have the potential for use in deriving mineral abundance from a reflectance spectrum.

Journal of Geophysical Research↗
USGS2011

Advances in hyperspectral remote sensing of vegetation and agricultural croplands

Recent advances in hyperspectral remote sensing (or imaging spectroscopy) demonstrate a great utility for a variety of land monitoring applications. It is now possible to be diagnostic in sensing species and plant communities using remotely sensed data and to do so in a direct and informed manner using modern tools and analyses. Hyperspectral data analyses are superior to traditional broadband analyses in spectral information. Many investigations explore and document remote sensing of vegetation and agricultural croplands. Some examples include (a) detecting plant stress [1], (b) measuring chlorophyll content of plants [2], (c) identifying small differences in percent of green vegetation cover [3], (d) extracting biochemical variables such as nitrogen and lignin [2,4–6], (e) discriminating land-cover types [7], (f) detecting crop moisture variations [8], (g) sensing subtle variations in leaf pigment concentrations [2,9,10], (h) modeling biophysical and yield characteristics of agricultural crops [6,11,12], (i) improving the detection of changes in sparse vegetation [13], and (j) assessing absolute water content in plant leaves [14]. This is a fairly detailed list but not exhaustive, meant to provide the reader with a measure of the current, proven experimental capabilities, and operational applications, and stimulate investigations of new, ambitious applications.

Book↗
USGS2024

Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry

Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R 2 ( p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.

Willamette River basin, Delaware River basin↗
USGS2007

Developing a flood monitoring system from remotely sensed data for the Limpopo basin

This paper describes the application of remotely sensed precipitation to the monitoring of floods in a region that regularly experiences extreme precipitation and flood events, often associated with cyclonic systems. Precipitation data, which are derived from spaceborne radar aboard the National Aeronautics and Space Administration's Tropical Rainfall Measuring Mission and from National Oceanic and Atmospheric Administration's infrared-based products, are used to monitor areas experiencing extreme precipitation events that are defined as exceedance of a daily mean areal average value of 50 mm over a catchment. The remotely sensed precipitation data are also ingested into a hydrologic model that is parameterized using spatially distributed elevation, soil, and land cover data sets that are available globally from remote sensing and in situ sources. The resulting stream-flow is classified as an extreme flood event when flow anomalies exceed 1.5 standard deviations above the short-term mean. In an application in the Limpopo basin, it is demonstrated that the use of satellite-derived precipitation allows for the identification of extreme precipitation and flood events, both in terms of relative intensity and spatial extent. The system is used by water authorities in Mozambique to proactively initiate independent flood hazard verification before generating flood warnings. The system also serves as a supplementary information source when in situ gauging systems are disrupted. This paper concludes that remotely sensed precipitation and derived products greatly enhance the ability of water managers in the Limpopo basin to monitor extreme flood events and provide at-risk communities with early warning information. ?? 2007 IEEE.

Limpopo River↗
USGS2003

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to realtime resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

Environmental Monitoring and Assessment↗
USGS2000

Verification of remotely sensed data

Ground or field checks are an important part of any remote sensing study and are necessary to provide an accurate and useful interpretive product. Field checking is necessary to confirm the validity of spectral, spatial, and morphological interpretations. In general, field checking should be done during all stages of any type of a remote sensing investigation. The methods and magnitude of work necessary to complete the field checking will be dependent on the type of remote sensing data to be verified and the scientific questions to be answered. Remotely sensed data provides an assessment of natural and anthropogenic features as they appear at the time of data acquisition, and possible changes between data acquisition and field checking must be considered.

Book chapter↗