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Results for “Remote Sensing Information”

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At least 595 records · Page 33Linked to original sources

USGS2000

Predicting broad-scale occurrences of vertebrates in patchy landscapes

Spatially explicit landscape-scale models that predict species distributions, where patches of habitat are shown as having potential to be occupied or unoccupied, are increasingly common. To successfully use such data, one should understand how these predicted distributions are created and how their relative accuracies are assessed. Geographic ranges, defined upon observations (e.g., atlases), literature review, and expert review, are a primary data layer. A map of land cover is created, often from interpretation of satellite imagery or other remotely-sensed data. Species/habitat associations are defined based upon a literature review and expert review, describing associations for habitats derived from the cover map. Included as ancillary associations are how species relate to physical features, where appropriate, such as elevation and hydrography. The three layers of information (range, land cover, and associations) are merged, often using raster-based algebraic statements that exclude unused habitats or patches outside the range of a species. The accuracy of predictions for a suite of species is typically assessed with surveys by comparing the species predicted to occur in an area to the species observed. Omission (i.e., present in species lists but not predicted) and commission (i.e., predicted but not present in lists) errors are reported. Errors may be due to many sources. For example, ranges of species change, cover types may be misidentified, species/habitat associations may be incorrect or change, or species may be rare and unlikely to be seen in surveys and judged in-error even though the species may be present. An example is given of an appropriate use of broad-scale species predicted distributions, in which patterns and threats to Maine terrestrial vertebrate diversity are summarized.

Landscape Ecology↗

USGS2010

Use of ground-based remotely sensed data for surface energy balance evaluation of a semiarid rangeland

An interdisciplinary field experiment was conducted to study the water and energy balance of a semiarid rangeland watershed in southeast Arizona during the summer of 1990. Two subwatersheds, one grass dominated and the other shrub dominated, were selected for intensive study with ground-based remote sensing systems and hydrometeorological instrumentation. Surface energy balance was evaluated at both sites using direct and indirect measurements of the turbulent fluxes (eddy correlation, variance, and Bowen ratio methods) and using an aerodynamic approach based on remote measurements of surface reflectance and temperature and conventional meteorological information. Estimates of net radiant flux density ( R n ), derived from measurements of air temperature, incoming solar radiation, and surface temperature and radiance compared well with values measured using a net radiometer (mean absolute difference (MAD) ≃ 50 W/m 2 over a range from 115 to 670 W/m 2 ). Soil heat flux density ( G ) was estimated using a relation between G / R n and a spectral vegetation index computed from the red and near-infrared surface reflectance. These G estimates compared well with conventional measurements of G using buried soil heat flux plates (MAD ≃ 20 W/m 2 over a range from −13 to 213 W/m 2 ). In order to account for the effects of sparse vegetation, semiempirical adjustments to the single-layer bulk aerodynamic resistance approach were required for evaluation of sensible heat flux density ( H ). This yielded differences between measurements and remote estimates of H of approximately 33 W/m 2 over a range from 13 to 303 W/m 2 . The resulting estimates of latent heat flux density, LE , were of the same magnitude and trend as measured values; however, a significant scatter was still observed: MAD ≃ 40 W/m 2 over a range from 0 to 340 W/m 2 . Because LE was solved as a residual, there was a cumulative effect of errors associated with remote estimates of R n , G , and H .

Water Resources Research↗

USGS2014

Landsat-7 Enhanced Thematic Mapper plus radiometric calibration

Landsat-7 is currently being built and tested for launch in 1998. The Enhanced Thematic Mapper Plus (ETM+) sensor for Landsat-7, a derivative of the highly successful Thematic Mapper (TM) sensors on Landsats 4 and 5, and the Landsat-7 ground system are being built to provide enhanced radiometric calibration performance. In addition, regular vicarious calibration campaigns are being planned to provide additional information for calibration of the ETM+ instrument. The primary upgrades to the instrument include the addition of two solar calibrators: the full aperture solar calibrator, a deployable diffuser, and the partial aperture solar calibrator, a passive device that allows the ETM+ to image the sun. The ground processing incorporates for the first time an off-line facility, the Image Assessment System (IAS), to perform calibration, evaluation and analysis. Within the IAS, processing capabilities include radiometric artifact characterization and correction, radiometric calibration from the multiple calibrator sources, inclusion of results from vicarious calibration and statistical trending of calibration data to improve calibration estimation. The Landsat Product Generation System, the portion of the ground system responsible for producing calibrated products, will incorporate the radiometric artifact correction algorithms and will use the calibration information generated by the IAS. This calibration information will also be supplied to ground processing systems throughout the world.

Canadian Journal of Remote Sensing↗

USGS2022

Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)

Rangelands have a dynamic response to climate change, fire, and other anthropogenic disturbances. The Rangeland Condition, Monitoring, Assessment, and Projection (RCMAP) product aims to capture this response by quantifying the percent cover of eight rangeland components, associated error, and trends across the western United States using Landsat from 1985 to 2020. The current generation of RCMAP has been improved with more training data, regional-scale Landsat composites, and more robust change detection. We assess the temporal patterns in each component with a linear model and a structural change method that determines break points using an 8-year temporal moving window. The linear and structural change methods generally agreed on patterns of change, but the latter found breaks more often, with at least one break point in most pixels. The structural change model provides more robust statistics on the significant minority of pixels with non-monotonic trends, while detrending some interannual signal potentially superfluous from a long-term perspective. Although break point density within one year of fire and vegetation treatments was ~10× and ~4× that of unburned areas, respectively, break point detection in the correct year of fire was only moderately accurate. Climate responses in break points proved more robust, with strong spatiotemporal relation in break point density with both aridity index values and aridity index change. Break point density strongly responds to both increased and decreased aridity and is reflective of ecosystem resilience. Data provide spatiotemporal information on the occurrence of breaks, but even more importantly, attribute those change events to specific component(s).

Arizona, California, Colorado, Idaho, Kansas, Mont↗

USGS2021

A new approach to evaluate and reduce uncertainty of model-based biodiversity projections for conservation policy formulation

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.

BioScience↗

USGS2016

Overview with methods and procedures of the U.S. Geological Survey mineral-resource assessment of the Sagebrush Focal Areas of Idaho, Montana, Nevada, Oregon, Utah, and Wyoming: Chapter A in Mineral resources of the Sagebrush Focal Areas of Idaho, Montana, Nevada, Oregon, Utah, and Wyoming

This report, chapter A of Scientific Investigations Report 2016–5089, provides an overview of the U.S. Geological Survey (USGS) Sagebrush Mineral-Resource Assessment (SaMiRA). The report also describes the methods, procedures, and voluminous fundamental reference information used throughout the assessment. Data from several major publicly available databases and other published sources were used to develop an understanding of the locatable, leaseable, and salable mineral resources of this vast area. This report describes the geologic, mineral-occurrence, geochemical, geophysical, remote-sensing, and Bureau of Land Management mineral-case-status data used for the assessment, along with the methods for evaluating locatable mineral-resource potential. The report also discusses energy-resource data (oil and gas, coal, and geothermal) used in the assessment. Appendixes include summary descriptive mineral-deposit models that provide the criteria necessary to assess for the pertinent locatable minerals and market-demand commodity profiles for locatable mineral commodities relevant to the project. Datasets used in the assessment are available as USGS data releases.

Idaho, Montana, Nevada, Oregon, Utah, Wyoming↗

USGS2010

Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics

Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Remote Sensing↗

USGS2005

Use of survey data to define regional and local priorities for management on National Wildlife Refuges

National Wildlife Refuges must manage habitats to support a variety of species that often have conflicting needs. To make reasonable management decisions, managers must know what species are priorities for their refuges and the relative importance of the species. Unfortunately, species priorities are often set regionally, but refuges must develop local priorities that reconcile regional priorities with constraints imposed by refuge location and local management options. Some species cannot be managed on certain refuges, and the relative benefit of management to regional populations of species can vary greatly among refuges. We describe a process of 'stepping down' regional priorities to local priorities for bird species of management interest. We define three primary scales of management interest: regional (at which overall priority species are set); 'Sepik Blocks' (30 min blocks of latitude and longitude, which provide a landscape level context for a refuge); and the refuge. Regional surveys, such as the North American Breeding Bird Survey, provide information that can be summarized at regional and Sepik Block scales, permitting regional priorities to be focused to landscapes near refuges. However, refuges manage habitats, and managers need information about how the habitat management is likely to collectively influence the priority species. The value of the refuge for a species is also influenced by the availability of habitats within refuges and the relative amounts of those habitats at each scale. We use remotely-sensed data to assess proportions of habitats at the three geographic scales. These data provide many possible approaches for developing local priorities for management. Once these are defined, managers can use the priorities, in conjunction with predictions of the consequences of management for each species, to assess the overall benefit of alternative management actions for the priority species.

Book chapter↗

USGS1987

New techniques for the quantification and modeling of remotely sensed alteration and linear features in mineral resource assessment studies

Linear structural features and hydrothermally altered rocks that were interpreted from Landsat data have been used by the U.S. Geological Survey (USGS) in regional mineral resource appraisals for more than a decade. In the past, linear features and alterations have been incorporated into models for assessing mineral resources potential by manually overlaying these and other data sets. Recently, USGS research into computer-based geographic information systems (GIS) for mineral resources assessment programs has produced several new techniques for data analysis, quantification, and integration to meet assessment objectives.

Pecora XI Symposium↗

USGS2004

An evaluation of gap-filled Landsat SLC-off imagery for wildland fire burn severity mapping

n May 31, 2003 unusual artifacts appeared within image data collected by the Enhanced Thematic Mapper plus (ETM+) instrument on-board the Landsat 7 spacecraft. The U.S. Geological Survey (USGS), with the support of NASA, has been working to find a means of compensating for the data gaps that result from a failure of the instrument’s scan line corrector (SLC). The SLC is an electromechanical device that compensates for the forward motion of the spacecraft by modifying the instrument’s optical path. The problem is likely due to a mechanical failure of the device for which there is no redundancy and that cannot be repaired or coaxed back into service. Further information regarding Landsat 7 and the SLC failure can be found at the Landsat Project home page (http://landsat7.usgs.gov).

Photogrammetric Engineering and Remote Sensing↗

USGS2026

Sentinel-2 for chlorophyll-a water quality monitoring: A review of validation evidence and application potential

Water quality monitoring is integral to preserving the health of freshwater ecosystems, and satellite remote sensing has emerged as one monitoring method. Sentinel-2, in particular, has been valuable for water quality monitoring due to its 5-day global temporal revisit time and spatial resolution that ranges from 10 to 60 metres. Sentinel-2 can be used to measure and monitor chlorophyll-a, which historically has been used as an indicator of water quality, eutrophication and harmful algal blooms. Our goal was to review aquatic chlorophyll-a Sentinel-2 research to assess the types of validation evidence reported. Validation evidence is defined here as the set of information key to assessing algorithm performance, and include the spatial and temporal scales of satellite validation, reported in situ sampling method context information, demonstration of validation results through plots, and appropriate algorithm performance metrics. We highlight how the body of literature collectively contributes to advancing a national scale chlorophyll-a product that could support future resource management applications. Our review of 122 published studies indicated that much of the validation evidence corresponded to early stages, as defined by the NASA data maturity framework, due to a limited focus on individual lakes and limited detail on methodology for reproducibility. Prioritizing data accessibility for both in situ data and satellite workflows used in published studies; reporting methods with transparency and consistency; and using standard algorithm performance metrics could provide a consistent framework to support and enhance the utility of satellite inland water quality research. These three quality assurance mechanisms can promote effective evaluation of approaches for remote sensing of chlorophyll-a. Adopting these quality criteria could enable the integration of validation evidence from multiple studies, supporting more spatially and temporally representative products that would advance these approaches towards maturation for broader application.

International Journal of Remote Sensing↗

USGS2015

Estimating concentrations of fine-grained and total suspended sediment from close-range remote sensing imagery

Fluvial sediment, a vital surface water resource, is hazardous in excess. Suspended sediment, the most prevalent source of impairment of river systems, can adversely affect flood control, navigation, fisheries and aquatic ecosystems, recreation, and water supply (e.g., Rasmussen et al., 2009; Qu, 2014). Monitoring programs typically focus on suspended-sediment concentration (SSC) and discharge (SSQ). These time-series data are used to study changes to basin hydrology, geomorphology, and ecology caused by disturbances. The U.S. Geological Survey (USGS) has traditionally used physical sediment sample-based methods (Edwards and Glysson, 1999; Nolan et al., 2005; Gray et al., 2008) to compute SSC and SSQ from continuous streamflow data using a sediment transport-curve (e.g., Walling, 1977) or hydrologic interpretation (Porterfield, 1972). Accuracy of these data is typically constrained by the resources required to collect and analyze intermittent physical samples. Quantifying SSC using continuous instream turbidity is rapidly becoming common practice among sediment monitoring programs. Estimations of SSC and SSQ are modeled from linear regression analysis of concurrent turbidity and physical samples. Sediment-surrogate technologies such as turbidity promise near real-time information, increased accuracy, and reduced cost compared to traditional physical sample-based methods (Walling, 1977; Uhrich and Bragg, 2003; Gray and Gartner, 2009; Rasmussen et al., 2009; Landers et al., 2012; Landers and Sturm, 2013; Uhrich et al., 2014). Statistical comparisons among SSQ computation methods show that turbidity-SSC regression models can have much less uncertainty than streamflow-based sediment transport-curves or hydrologic interpretation (Walling, 1977; Lewis, 1996; Glysson et al., 2001; Lee et al., 2008). However, computation of SSC and SSQ records from continuous instream turbidity data is not without challenges; some of these include environmental fouling, calibration, and data range among sensors. Of greatest interest to many programs is a hysteresis in the relationship between turbidity and SSC, attributed to temporal variation of particle size distribution (Landers and Sturm, 2013; Uhrich et al., 2014). This phenomenon causes increased uncertainty in regression-estimated values of SSC, due to changes in nephelometric reflectance off the varying grain sizes in suspension (Uhrich et al., 2014). Here, we assess the feasibility and application of close-range remote sensing to quantify SSC and particle size distribution of a disturbed, and highly-turbid, river system. We use a consumer-grade digital camera to acquire imagery of the river surface and a depth-integrating sampler to collect concurrent suspended-sediment samples. We then develop two empirical linear regression models to relate image spectral information to concentrations of fine sediment (clay to silt) and total suspended sediment. Before presenting our regression model development, we briefly summarize each data-acquisition method.

Conference Paper↗

USGS1976

Access routes to the U.S. Geological Survey's EROS Data Center, Sioux Falls, South Dakota

The EROS Data Center is a part of the Earth Resources Observation Systems (EROS) Program of the Department of the Interior, managed by the U.S. Geological Survey. It is the national center for the processing anddissemination of spacecraft and aircraft acquired photographic imagery and electronic data of the Earth's resources. The center also trains and assists users in the application of such data. The EROS Data Center provides access to Landsat data, aerial photography acquired by the U.S. Department of the Interior, and photography and other remotely sensed data acquired by the National Aeronautics and Space Administration (NASA) from research aircraft and from Skylab, Apollo, and Gemini spacecraft.

South Dakota↗

USGS2010

U.S. Geological Survey Science for the Wyoming Landscape Conservation Initiative-2009 Annual Report

This is the second report produced by the U.S. Geological Survey (USGS) for the Wyoming Landscape Conservation Initiative (WLCI) to detail annual work activities. The first report described work activities for 2007 and 2008; this report covers work activities conducted in 2009. Important differences between the two reports are that (1) this report does not lump all the Effectiveness Monitoring activities together as last year's report did, which will allow WLCI partners and other readers to fully appreciate the scope and accomplishments of those activities, and (2) this report does not include a comprehensive appendix of the background details for each work activity. In 2009, there were 29 ongoing or completed activities, and there were 5 new work activities conducted under the 5 original major multi-disciplinary science and technical assistance activities: (1) Baseline Synthesis; (2) Targeted Monitoring and Research; (3) Data and Information Management; (4) Integration and Coordination; and (5) Decisionmaking and Evaluation. New work included (1) developing a soil-quality index, (2) developing methods for assessing levels of and relationships between mercury and soil organic matter, and (3) ascertaining element source, mobility, and fate. Additionally, (4) remotely sensed imagery was used to assess vegetation as an indicator of soil condition and geology, and (5) an Integrated Assessment (IA) was initiated to synthesize what has been learned about WLCI systems to date, and to develop associated decision tools, maps, and a comprehensive report.

Open-File Report↗

USGS2021

Monitoring volcanic deformation

Deformation signals recorded at volcanoes have long been used to infer the processes behind subsurface magma intrusions. Monitoring strategies vary greatly depending on several factors such as the activity of the individual volcano, access, available personnel, and funding. Certain geodetic monitoring methods, such as Electronic Distance Measurements, are inexpensive but require that scientists be dangerously close to active areas. Other techniques, such as telemetered geodetic measurements (Electronic Tiltmeters and Global Navigation Satellite System), or deformation images from Interferometric Synthetic Aperture Radar, can be collected remotely and with less risk. Observed surface deformation can be fit to the predictions of mathematical source models to obtain quantitative estimates of their parameters (e.g., location, depth, volume change and more). Combined deformation and gravity change measurements can be used to infer the density of subsurface intrusions and better constrain the source of unrest. To be effective, geodetic monitoring must be done before, during, and after eruptions and must be integrated with other monitoring techniques (e.g., seismology, geochemistry, physical volcanology, remote sensing). It requires the long-term commitment of time and resources. Done effectively, geodetic monitoring not only can provide timely warnings of escalating volcano hazards but may also lead to improved understanding of how volcanoes work. Even when a volcano is not active, monitoring generates baseline information against which changes in volcano behavior can be compared. Preserving the integrity and accessibility of geodetic data archives is thus essential if future volcanologists are to benefit from the decades-long records of geodetic data gathered by volcano observatories.

Book chapter↗

USGS2012

Flood pulsing in the Sudd wetland: analysis of seasonal variations in 2 inundation and evapotranspiration in Southern Sudan

Located on the Bahr el Jebel in South Sudan, the Sudd is one of the largest floodplain wetlands in the world. Seasonal inundation drives the hydrologic, geomorphological, and ecological processes, and the annual flood pulse is essential to the functioning of the Sudd. Despite the importance of the flood pulse, various hydrological interventions are planned upstream of the Sudd to increase economic benefits and food security. These will not be without consequences, in particular for wetlands where the biological productivity, biodiversity, and human livelihoods are dependent on the flood pulse and both the costs and benefits need to be carefully evaluated. Many African countries still lack regional baseline information on the temporal extent, distribution, and characteristics of wetlands, making it hard to assess the consequences of development interventions. Because of political instability in Sudan and the inaccessible nature of the Sudd, recent measurements of flooding and seasonal dynamics are inadequate. Analyses of multitemporal and multisensor remote sensing datasets are presented in this paper, in order to investigate and characterize flood pulsing within the Sudd wetland over a 12-month period. Wetland area has been mapped along with dominant components of open water and flooded vegetation at five time periods over a single year. The total area of flooding (both rain and river fed) over the 12 months was 41 334 km2, with 9176 km2 of this constituting the permanent wetland. Mean annual total evaporation is shown to be higher and with narrower distribution of values from areas of open water (1718 mm) than from flooded vegetation (1641 mm). Although the exact figures require validation against ground-based measurements, the results highlight the relative differences in inundation patterns and evaporation across the Sudd.

Earth Interactions↗

USGS2003

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Photogrammetric Engineering and Remote Sensing↗

USGS2021

Monitoring climate impacts on annual forage production across U.S. semi-arid grasslands

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha −1 year −1 ) using a newly-developed relation with the Soil Survey Geographic Database range production data (R 2 = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R 2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers.

Remote Sensing↗