USGS Science⌕ Search

SEARCH · USGS Science

Results for “Remote Sensing Information”

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

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

At least 73 records · Page 4Linked to original sources
USGS2013

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

International Journal of Applied Earth Observation↗
USGS2012

Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods

We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.

Wyoming↗
USGS2017

A framework for modeling connections between hydraulics, water surface roughness, and surface reflectance in open channel flows

This paper introduces a framework for examining connections between the flow field, the texture of the air-water interface, and the reflectance of the water surface and thus evaluating the potential to infer hydraulic information from remotely sensed observations of surface reflectance. We used a spatial correlation model describing water surface topography to illustrate the application of our framework. Nondimensional relations between model parameters and flow intensity were established based on a prior flume study. Expressing the model in the spatial frequency domain allowed us to use an efficient Fourier transform-based algorithm for simulating water surfaces. Realizations for both flume and field settings had water surface slope distributions positively correlated with velocity and water surface roughness. However, most surface facets were gently sloped and thus unlikely to yield strong specular reflections; the model exaggerated the extent of water surface features, leading to underestimation of facet slopes. A ray tracing algorithm indicated that reflectance was greatest when solar and view zenith angles were equal and the sensor scanned toward the Sun to capture specular reflections of the solar beam. Reflected energy was concentrated in a small portion of the sky, but rougher water surfaces reflected rays into a broader range of directions. Our framework facilitates flight planning to avoid surface-reflected radiance while mapping other river attributes, or to maximize this component to exploit relationships between hydraulics and surface reflectance. This initial analysis also highlighted the need for improved models of water surface topography in natural rivers.

Journal of Geophysical Research F: Earth Surface↗
USGS2018

Quantifying variance across spatial scales as part of fire regime classifications

The emergence of large‐scale fire classifications and products informed by remote sensing data has enabled opportunities to include variability or heterogeneity as part of modern fire regime classifications. Currently, basic fire metrics such as mean fire return intervals are calculated without considering spatial variance in a management context. Fire return intervals are also only applicable at a particular grain size (defined as the spatial unit of interest) even though they are typically applied homogeneously. In this study, we utilized a 29‐yr fire occurrence database to show how spatial variance changes with respect to grain as postulated by Wiens ( 1989 ) when reporting fire patterns within the Great Plains, USA. We utilized data from the Monitoring Trends in Burn Severity database of fire occurrence for the years 1984–2012. We analyzed median numbers of fire along with their variance at four spatial grains ranging from small units (e.g., plots at 3 × 3 km resolution) to large units (e.g., landscapes at 1500 × 2700 km resolution). Median number of fire occurrences was consistently low, irrespective of grain. Despite the consistency in low median numbers of fires across grain, variance in the numbers of fires between units decreased. Variance within units, however, did not change as grain increased indicating fire‐pattern‐scale inconsistencies. Fire pattern interpretations depended entirely on the scale at which it is calculated. Given that the Great Plains region has a large disparity in fire patterns (i.e., some regions burn often, while others may never burn), fire regime classifications will benefit from including scale‐specific variance estimates as a foundation for understanding changes in fire regimes and corresponding social–ecological and policy responses.

Ecosphere↗
USGS2015

Trends in publications in fluvial geomorphology over two decades: A truly new era in the discipline owing to recent technological revolution?

Trends in the field of fluvial geomorphology have been reviewed by a number of authors, who have emphasized the dramatic change occuring in the field in the last two decades of the twentieth century, largely as a result of technological advances. Nevertheless, no prior authors have systematically compiled data on publications in fluvial geomorphology over a long period and statistically analyzed the resulting data set. In this contribution we present a quantitative analysis of fluvial geomorphology papers published in the twenty-two-year period 1987–2009 in five journals of the discipline with a more specific focus on Geomorphology and Earth Surface Processes and Landforms ( ESPL ), identifying authorships, geographic origin of authors, and spatial and temporal scales covered. We also documented the tools employed, demonstrating the transformation of the field with the emergence of new tools over this period, and conducted a cluster to highlight links between tools and a set of factors (country of author's origin, journals, time, and spatial and temporal scales). Of the 1717 papers published in the five journals during this period, the results showed an increased diversity in the nationality of the first author, mainly when dealing with present time scale, and channel feature. Our data show a significant change in methods used in the field as a result of the increase in data availability and new sources of information from remote sensing (ground, airborne and, satellite). Clearly, a new era in knowledge production is observed since 2000, showing the emergence of a second period of active quantification and an internationalization of the fields.

Geomorphology↗
USGS2001

Spatial variability of sediment erosion processes using GIS analysis within watersheds in a historically mined region, Patagonia Mountains, Arizona

In this study, a geographic information system (GIS) is used to integrate and accurately map field studies, information from remotely sensed data, watershed models, and the dispersion of potentially toxic mine waste and tailings. The purpose of this study is to identify erosion rates and net sediment delivery of soil and mine waste/tailings to the drainage channel within several watershed regions to determine source areas of sediment delivery as a method of quantifying geo-environmental analysis of transport mechanisms in abandoned mine lands in arid climate conditions. Users of this study are the researchers interested in exploration of approaches to depicting historical activity in an area which has no baseline data records for environmental analysis of heavily mined terrain.

Arizona↗
USGS2003

Historical and projected coastal Louisiana land changes: 1978-2050

An important component of the Louisiana Coastal Area (LCA) Comprehensive Coastwide Ecosystem Restoration Study is the projection of a “future condition” for the Louisiana coast if no further restoration measures were adopted. Such a projection gives an idea of what the future might hold without implementation of the LCA plan and provides a reference against which various ecosystem restoration proposals can be assessed as part of the planning process. One of the most fundamental measures of ecosystem degradation in coastal Louisiana has been the conversion of land (mostly emergent vegetated habitat) to open water. Thus, the projection of the future condition of the ecosystem must be based upon the determination of future patterns of land and water. To conduct these projections, a multidisciplinary LCA Land Change Study Group was formed that included individuals from agencies and academia with expertise in remote sensing, geographic information systems (GIS), ecosystem processes, and coastal land loss. Methods were based upon those used in prior studies for Coast 2050 (Louisiana Coastal Wetlands Conservation and Restoration Task Force [LCWCRTF] and the Wetlands Conservation and Restoration Authority 1998, 1999) and modified as described here to incorporate an improved understanding of coastal land loss and land gain processes with more advanced technical capabilities. The basic approach is to use historical data to assess recent trends in land loss and land gain and to project those changes into the future, taking into account spatial variations in the patterns and rates of land loss and land gain. This approach is accomplished by developing a base map, assessing and delineating areas of similar land change (polygons), and projecting changes into the future. This report describes the methodology and compares the current land change projection to previous projections.

Open-File Report↗
USGS2013

A one-dimensional diffusion analogy model for estimation of tide heights in selected tidal marshes in Connecticut

A one-dimensional diffusion analogy model for estimating tide heights in coastal marshes was developed and calibrated by using data from previous tidal-marsh studies. The method is simpler to use than other one- and two-dimensional hydrodynamic models because it does not require marsh depth and tidal prism information; however, the one-dimensional diffusion analogy model cannot be used to estimate tide heights, flow velocities, and tide arrival times for tide conditions other than the highest tide for which it is calibrated. Limited validation of the method indicates that it has an accuracy within 0.3 feet. The method can be applied with limited calibration information that is based entirely on remote sensing or geographic information system data layers. The method can be used to estimate high-tide heights in tidal wetlands drained by tide gates where tide levels cannot be observed directly by opening the gates without risk of flooding properties and structures. A geographic information system application of the method is demonstrated for Sybil Creek marsh in Branford, Connecticut. The tidal flux into this marsh is controlled by two tide gates that prevent full tidal inundation of the marsh. The method application shows reasonable tide heights for the gates-closed condition (the normal condition) and the one-gate-open condition on the basis of comparison with observed heights. The condition with all tide gates open (two gates) was simulated with the model; results indicate where several structures would be flooded if the gates were removed as part of restoration efforts or if the tide gates were to fail.

Connecticut↗
USGS2014

Assessment of the spatial extent and height of flooding in Lake Champlain during May 2011, using satellite remote sensing and ground-based information

Landsat 5 and moderate resolution imaging spectro-radiometer satellite imagery were used to map the area of inundation of Lake Champlain, which forms part of the border between New York and Vermont, during May 2011. During this month, the lake’s water levels were record high values not observed in the previous 150 years. Lake inundation area determined from the satellite imagery is correlated with lake stage measured at three U.S. Geological Survey lake level gages to provide estimates of lake area at different lake levels (stage/area rating) and also compared with the levels of the high-water marks (HWMs) located on the Vermont side of the lake. The rating developed from the imagery shows a somewhat different relation than a similar stage/area rating developed from a medium-resolution digital elevation model (DEM) of the region. According to the rating derived from the imagery, the lake surface area during the peak lake level increased by about 17 percent above the average or “normal” lake level. By using a comparable rating developed from the DEM, the increase above average is estimated to be about 12 percent. The northern part of the lake (north of Burlington) showed the largest amount of flooding. Based on intersecting the inundation maps with the medium-resolution DEM, lake levels were not uniform around the lake. This is also evident from the lake level gage measurements and HWMs. The gage data indicate differences up to 0.5 feet between the northern and southern end of the lake. Additionally, the gage data show day-to-day and intradaily variation of the same range (0.5 foot). The high-water mark observations show differences up to 2 feet around the lake, with the highest level generally along the south- and west-facing shorelines. The data suggest that during most of May 2011, water levels were slightly higher and less variable in the northern part of the lake. These phenomena may be caused by wind effects as well as proximity to major river inputs to the lake. The inundation areas generated from the imagery generally coincide with flood mapping as estimated by the Federal Emergency Management Agency (FEMA) and shown on its digital flood insurance rate maps. Where areas in the flood inundation map derived from the imagery and the FEMA estimated flooded areas differ substantially, this difference may be due to differences between the flood magnitude at the time of the image and the assumed flood condition used for the FEMA modeling and mapping, wind/storage effects not accounted for by the FEMA modeling, and the resolution of the image compared to the DEM used in the FEMA mapping.

Lake Champlain↗
USGS2023

Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: informing Tribal restoration priorities

Restoring degraded river systems is an enormous challenge, especially given the uncertainty in a time of climate change. Here, Roy Petrakis explains how restoration approaches informed by remote sensing and a climate adaptation framework increase the potential for overall success. He discusses research being done on the Gila River as a case study of how it might work.

Arizona, New Mexico↗
USGS2024

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Alaska↗
USGS1996

Developing a temporal database of urban development for the Baltimore/Washington region

The U.S. Geological Survey (USGS), the University of Maryland Baltimore County (UMBC), and the U.S. Bureau of the Census are working together as a multiagency, multidisciplinary team in developing a temporal database that documents the growth of the Baltimore-Washington metropolitan region. This database consists of urban development, principal transportation, shoreline, and population density change. The urban development theme, considered a primary data layer in the study of urban land transformation resulting from human impact on the land, is the focus of this paper. The Baltimore-Washington Spatial Dynamics and Human Impacts Study builds on earlier research efforts that mapped urban land use change for the San Francisco Bay area (Acevedo and Bell, 1994; Bell and others, 1995; Kirtland and others, 1994). In developing a temporal database (Acevedo and others, in press), the team participants hope to provide data that can be used to study patterns of urban growth; assess ecological, environmental, and climatic impacts of urban change; and model and predict future urbanization patterns and impacts (Clarke and others, 1996). Both the San Francisco and Baltimore-Washington regions were selected because of the rapid urban growth and resulting impacts on their ecosystems. The Chesapeake Bay region in particular has undergone extensive environmental agitation due to the hydrologic problems that have arisen from the increase in impermeable surfaces and structures, that is buildings and pavement that physically cover the soil. Because of the inability of water to percolate into the ground, little purification occurs by filtration. Water runs over paved surfaces and quickly washes high levels of toxins directly into the water system. Toxins like gasoline, oil, and fertilizer have dramatically affected the local streams, rivers, and the bay. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. This paper describes the techniques used to map the extent of urban areas for Phase I and does not discuss Phase II in detail because the work is still in progress. In this study, urban development is defined as areas of intensive use, with much of the land covered by structures. The built-up areas are characterized by the existence of a systematic street pattern, and the relative concentration of buildings and associated intensive use areas, such as parking lots. Using this definition, urban development does not refer to political boundaries and may include incorporated or unincorporated areas as well as military reservations. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. To build the urban component of the temporal database, a multidisciplinary team was assembled and a phased approach initiated. Expanding on procedures developed for the San Francisco Regional Study (Bell and others, 1995), the team developed data definitions, a classification scheme, compilation criteria, mapping specifications, guidelines for source materials, and metadata specifications to support development of a logically consistent dataset. Extensive documentation procedures were established to ensure consistency in data collection, and for subsequent application to other regions. Phase II was the implementation of the regional mapping effort. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. The study area for Phase I consisted of an approximate area of 15- by 15-minute segment centered around the city of Baltimore (fig. 1). Phase I was used as a prototype for the technique development and integration that the multiagency collaborative effort would require. The regional study, Phase II, encompassed a 2-degree square centered on Washington, D.C. With more than 7 million people spread across 39 counties, the Baltimore-Washington region is one the Nation's fastest growing metropolitan areas. The two cities are rapidly merging into one.

Maryland↗
USGS2012

Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling

This paper discusses spatial aspects of the global exposure dataset and mapping needs for earthquake risk assessment. We discuss this in the context of development of a Global Exposure Database for the Global Earthquake Model (GED4GEM), which requires compilation of a multi-scale inventory of assets at risk, for example, buildings, populations, and economic exposure. After defining the relevant spatial and geographic scales of interest, different procedures are proposed to disaggregate coarse-resolution data, to map them, and if necessary to infer missing data by using proxies. We discuss the advantages and limitations of these methodologies and detail the potentials of utilizing remote-sensing data. The latter is used especially to homogenize an existing coarser dataset and, where possible, replace it with detailed information extracted from remote sensing using the built-up indicators for different environments. Present research shows that the spatial aspects of earthquake risk computation are tightly connected with the availability of datasets of the resolution necessary for producing sufficiently detailed exposure. The global exposure database designed by the GED4GEM project is able to manage datasets and queries of multiple spatial scales.

Natural Hazards↗
USGS2023

Causality-informed Bayesian inference for rapid seismic ground failure and building damage estimation

Rapid and accurate estimates of seismic ground failure and building damage are beneficial to efficient emergency response and post-earthquake recovery. Traditional approaches, such as physical and geospatial models, have poor accuracy and resolution due to large uncertainties and the limited availability of informing geospatial layers. The introduction of remote sensing techniques has shown potential in providing supplementary information for rapid hazard estimation by analyzing earthquake-induced correlation changes between pre- and post-event satellite images. However, the changes in satellite images are the result of overlapping ground failure, building damage, and environmental noise, making it challenging to categorize and estimate different seismic hazards and impacts directly from satellite images.Here we design a novel causality-informed Bayesian network that continuously updates seismic ground failure and building damage estimates from satellite images by modeling the physical interdependencies between geospatial features, ground failure, building footprints, building damage, and satellite images. The incorporation of physical interdependencies allows an effective fusion of physical models and rich but noisy information from remote sensing observations and reduces bias and uncertainties in estimations. Our experiments show that integrating satellite images through our Bayesian network improves the accuracy of seismic ground failure and building damage estimations.

Conference Paper↗
USGS2026

End-user needs for remote sensing wetlands of the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America comprises globally important grassland and wetland ecosystems critical for numerous populations of migratory birds. Due to the importance of this region for migratory birds, and particularly waterfowl, and the threats of habitat loss due to intensifying agriculture, there is a mature and diverse system of conservation organizations, agencies, and partnerships that spends hundreds of millions of dollars annually on habitat conservation to support migratory bird populations. Remote sensing can be a powerful tool for observing and evaluating global change at large scales as well as expanding inferences from field studies to the broader landscape with statistical models. However, development and utilization of these tools has lagged behind their demand for several reasons, including concerns over spatial and temporal resolution and accuracy of products; perception of a misalignment with decision-maker needs; technological barriers such as skill sets of conservation professionals, computing resources, data access, and usability. In this report, we summarize the needs of conservation professionals and scientists who use or want to use remote sensing data products to inform science about wetland change and conservation of wetlands in the PPR. We assembled this information through several methods leading up to, during, and following a January 2026 PPR Wetland Remote Sensing Workshop. The workshop included United States and Canadian scientists, conservation professionals, and policy experts. Our goal was to bring together end-users and remote sensing product developers jointly to explore reducing the lag between product development and utilization of products to inform science and conservation. Specifically, we aimed to identify gaps in wetland remote sensing that limit effective monitoring, management, and conservation in the PPR, and to develop a framework that outlines pathways to address these gaps by fostering collaboration, improving communication networks, encouraging discussion, and building on existing and ongoing efforts. This report summarizes our participants’ descriptions of end-user needs and the outcomes of the workshop.

Prairie Pothole region↗