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At least 217 records · Page 12Linked to original sources
USGS1972

ERTS and EROS

In June the National Aeronautics & Space Administration is to launch its first experimental satellite designed to view the Earth systematically with remote-sensing instruments that will provide new information about our resources and environment. The launching will culminate more than 8 years of planning and research by resource agencies of the Federal Government in cooperation with NASA, state and local governments, universities, and industry. The first Earth Resources Technology Satellite, ERTS-A, will be followed a year later by ERTS-B. Analyses of data from them, it is hoped, will lead to design of operational satellites for Earth resources investigations in the future. In the belief that satellite systems will be of significant assistance in meeting its responsibilities to map, monitor, and manage the vast resources and the public lands of the United States, the Department of the Interior assumed a major role in the ERTS-A Experiment.

Geotimes↗
USGS2006

Remote sensing for grassland management in the arid Southwest

We surveyed a group of rangeland managers in the Southwest about vegetation monitoring needs on grassland. Based on their responses, the objective of the RANGES (Rangeland Analysis Utilizing Geospatial Information Science) project was defined to be the accurate conversion of remotely sensed data (satellite imagery) to quantitative estimates of total (green and senescent) standing cover and biomass on grasslands and semidesert grasslands. Although remote sensing has been used to estimate green vegetation cover, in arid grasslands herbaceous vegetation is senescent much of the year and is not detected by current remote sensing techniques. We developed a ground truth protocol compatible with both range management requirements and Landsat's 30 m resolution imagery. The resulting ground-truth data were then used to develop image processing algorithms that quantified total herbaceous vegetation cover, height, and biomass. Cover was calculated based on a newly developed Soil Adjusted Total Vegetation Index (SATVI), and height and biomass were estimated based on reflectance in the near infrared (NIR) band. Comparison of the remotely sensed estimates with independent ground measurements produced r2 values of 0.80, 0.85, and 0.77 and Nash Sutcliffe values of 0.78, 0.70, and 0.77 for the cover, plant height, and biomass, respectively. The approach for estimating plant height and biomass did not work for sites where forbs comprised more than 30% of total vegetative cover. The ground reconnaissance protocol and image processing techniques together offer land managers accurate and timely methods for monitoring extensive grasslands. The time-consuming requirement to collect concurrent data in the field for each image implies a need to share the high fixed costs of processing an image across multiple users to reduce the costs for individual rangeland managers.

Rangeland Ecology and Management↗
USGS1995

History and use of remote sensing for conservation and management of federal lands in Alaska, USA

Remote sensing has been used to aid land use planning efforts for federal public lands in Alaska since the 1940s. Four federal land management agencies-the U.S. Fish and Wildlife Service, US. Bureau of Land Management, US. National Park Service, and U.S. Forest Service-have used aerial photography and satellite imagery to document the extent, type, and condition of Alaska's natural resources. Aerial photographs have been used to collect detailed information over small to medium-sized areas. This standard management tool is obtainable using equipment ranging from hand-held 35-mm cameras to precision metric mapping cameras. Satellite data, equally important, provide synoptic views of landscapes, are digitally manipulatable, and are easily merged with other digital databases. To date, over 109.2 million ha (72%) of Alaska's land cover have been mapped via remote sensing. This information has provided a base for conservation, management, and planning on federal public lands in Alaska.

Alaska↗
USGS2024

Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools

Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.

Montana↗
USGS1982

An assessment of Landsat data acquisition history on identification and area estimation of corn and soybeans

In the past decade, numerous studies have demonstrated the potential of satellite remote sensing for providing accurate timely crop area information. This study assessed the impact of Landsat data acquisition history on classification and area estimation accuracy of corn and soybeans in the U.S. Corn Belt. The results illustrate the importance of selecting Landsat acquisitions based on spectral differences in crops at certain development stages. Although early season information can provide estimates of total corn and soybean areas, acquisitions from about emergence and after tasseling of the corn seem to provide a minimal set for accurate identification of corn and soybeans in the U.S. Corn Belt. Additional acquisitions provide only marginally greater separability for corn and soybeans.

Corn Belt↗
USGS2015

Global view of remote sensing of rangelands: Evolution, applications, future pathways

The term “rangeland” is rather nebulous, and there is no single definition of rangeland that is universally accepted by land managers, scientists, or international bodies (Lund, 2007; Reeves and Mitchell, 2011). Dozens and possibly hundreds (Lund, 2007) of definitions and ideologies exist because various stakeholders often have unique objectives requiring different information. For the purpose of describing the role of remote sensing in a global context, it is, however, necessary to provide definitions to orient the reader. The Food and Agricultural Organization (FAO) of the United Nations convened a conference in 2002 and again in 2013 to begin addressing the issue of harmonizing definitions of forest- related activities. Based on this concept, here rangelands are considered lands usually dominated by nonforest vegetation. The Society for Range Management defines rangelands as (SRM, 1998) Land on which the indigenous vegetation (climax or natural potential) is predominantly grasses, grass-like plants, forbs, or shrubs and is managed as a natural ecosystem. If plants are introduced, they are managed similarly. Rangelands include natural grasslands, savannas, shrublands, many deserts, tundra, alpine communities, marshes, and wet meadows.

Book chapter↗
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↗
USGS2025

System characterization report on Resourcesat-2A Linear Imaging Self Scanning-4 sensor

Executive Summary This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-4 (LISS–4) sensor. It is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resouresat-2 and 2A carry the Advanced Wide Field Sensor, Linear Imaging Self Scanning-3, and LISS–4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about the Indian Space Research Organisation’s satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium at https://calval.cr.usgs.gov/apps/compendium/ and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performances of the Resourcesat-2A LISS–4 sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had mean offsets in the range of −0.118 to 0.024 pixel in easting and −0.053 to 0.022 pixel in northing with root mean square error values from 0.067 to 0.230 pixel in easting and from 0.087 to 0.2 pixel in northing. The exterior geometric performance had offsets in the range of 2.55 to 7.85 meters (m) in easting and −6.15 to 11.15 m in northing with root mean square error values in the range of 2.6 to 8.2 m in easting and 6.35 to 11.8 m in northing compared to the U.S. Department of Agriculture National Agriculture Imagery Program and WorldView-3 orthoimages. The measured radiometric performance had offsets from 0.003 to 0.024 and slopes from 0.736 to 0.952, and spatial performance was in the range of 1.633 to 1.903 pixels for the full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.0529 to 0.0952.

Open-File Report↗
USGS2025

System characterization report on Resourcesat-2A Advanced Wide Field Sensor

Executive Summary This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resourcesat-2 and -2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about Indian Space Research Organisation satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium and from the Indian Space Research Organisation at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performance of the Resourcesat-2A AWiFS sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had offsets in the range of −1.10 meters (m; −0.020 pixel) to 3.67 m (0.066 pixel) in easting and −5.68 m (−0.101 pixel) to 10.38 m (0.185 pixel) in northing with root mean square error values from 5.60 m (0.100 pixel) to 11.31 m (0.202 pixel) in easting and from 3.00 m (0.054 pixel) to 13.52 m (0.241 pixel) in northing. The exterior geometric performance had mean offsets of −25.29 m in easting and 16.22 m northing with root mean square error values of 26.07 m in easting and 17.60 m in northing compared to the Landsat 8 Operational Land Imager sensor. The radiometric performance had offsets from −0.002 to 0.029 and slopes from 0.733 to 1.012. Spatial performance was in the range of 1.354 to 1.639 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.108 to 0.174.

Open-File Report↗
USGS2024

Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km 2 ) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m 3 /m 3 and a bias of 0.02 m 3 /m 3 . Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.

California↗
USGS2012

EarthExplorer

The EarthExplorer trifold provides basic information for on-line access to remotely-sensed data from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center archive. The EarthExplorer (http://earthexplorer.usgs.gov/) client/server interface allows users to search and download aerial photography, satellite data, elevation data, land-cover products, and digitized maps. Minimum computer system requirements and customer service contact information also are included in the brochure.

General Information Product↗
USGS2020

New operational national satellite burned area product

Introduction Lack of consistent spatial and temporal fire information with relevant spatial resolution hinders land management and broad-scale assessments of fire activity, especially in the eastern United States and the Great Plains where fi re is important ecologically and culturally. Remote sensing can be used to monitor fi re activity, augment existing fi re data, and fill information gaps. In particular, Landsat offers one of the most complete time series of remote sensing data sets as the Landsat satellites with spectral bands useful for mapping fi res and burn severity have been operational since 1984. Furthermore, Landsat satellite imagery collect data at a resolution useful for on-the-ground comparisons and management decisions. Methods A gradient-boosting regression model algorithm was used to predict burn probabilities (BP), indicating the likelihood that a pixel had burned in a fire. Then the algorithm translated the burn probability images to burn classification (BC) images using thresholding and region growing. Burned areas smaller than 5 acres were removed to reduce noise. The BP and BC products were generated for Landsat scenes collected from 1984 to present with <= 80% cloud cover and are available for download from USGSs EarthExplorer website (www.earthexplorer. usgs.gov). Annual summaries were also produced from the scene-level BC and BP products. These include: 1) the maximum burn probability value from all scenes in a year, (2) the burn classification count, or the number of times a pixel was classified as burned in a year, (3) the burn date, or the day of year of the first Landsat scene a pixel was classified as burned, and (4) a burn classification which removes burned areas still visible from previous years with a filter. Results The Landsat Burned Area Products identified 183% more burned area than the Monitoring Trends in Burn Severity and 56% more burned area than the MODIS MCD64A1.006 products2. From 1984 through 2018, annual burned area mapped by the Landsat Burned Area Products averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. The Landsat Burned Area products consistently map burned areas 5 acres or larger over time while capturing unburned islands within fi res. Such characteristics are critical for understanding long-term impacts of fi res on human and natural systems. The products are being routinely produced as new Landsat data are collected and demonstrate the utility remote sensing offers for monitoring landscape change.

Report↗
USGS2023

AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the ‘Automated Interactive Monitoring System (AIMS) for Wildlife’. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.

Journal of Environmental Management↗
USGS2012

United States Geological Survey (USGS) Natural Hazards Response

The primary goal of U.S. Geological Survey (USGS) Natural Hazards Response is to ensure that the disaster response community has access to timely, accurate, and relevant geospatial products, imagery, and services during and after an emergency event. To accomplish this goal, products and services provided by the National Geospatial Program (NGP) and Land Remote Sensing (LRS) Program serve as a geospatial framework for mapping activities of the emergency response community. Post-event imagery and analysis can provide important and timely information about the extent and severity of an event. USGS Natural Hazards Response will also support the coordination of remotely sensed data acquisitions, image distribution, and authoritative geospatial information production as required for use in disaster preparedness, response, and recovery operations.

Fact Sheet↗
USGS2007

FFT-enhanced IHS transform method for fusing high-resolution satellite images

Existing image fusion techniques such as the intensity-hue-saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. ?? 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2012

Influence of permafrost distribution on groundwater flow in the context of climate-driven permafrost thaw: Example from Yukon Flats Basin, Alaska, United States

Understanding the role of permafrost in controlling groundwater flow paths and fluxes is central in studies aimed at assessing potential climate change impacts on vegetation, species habitat, biogeochemical cycling, and biodiversity. Recent field studies in interior Alaska show evidence of hydrologic changes hypothesized to result from permafrost degradation. This study assesses the hydrologic control exerted by permafrost, elucidates modes of regional groundwater flow for various spatial permafrost patterns, and evaluates potential hydrologic consequences of permafrost degradation. The Yukon Flats Basin (YFB), a large (118,340 km 2 ) subbasin within the Yukon River Basin, provides the basis for this investigation. Model simulations that represent an assumed permafrost thaw sequence reveal the following trends with decreasing permafrost coverage: (1) increased groundwater discharge to rivers, consistent with historical trends in base flow observations in the Yukon River Basin, (2) potential for increased overall groundwater flux, (3) increased spatial extent of groundwater discharge in lowlands, and (4) decreased proportion of suprapermafrost (shallow) groundwater contribution to total base flow. These trends directly affect the chemical composition and residence time of riverine exports, the state of groundwater-influenced lakes and wetlands, seasonal river-ice thickness, and stream temperatures. Presently, the YFB is coarsely mapped as spanning the continuous-discontinuous permafrost transition that model analysis shows to be a critical threshold; thus, the YFB may be on the verge of major hydrologic change should the current permafrost extent decrease. This possibility underscores the need for improved characterization of permafrost and other hydrogeologic information in the region via geophysical techniques, remote sensing, and ground-based observations.

Alaska↗
USGS2012

Enhanced visualization for the interpretation of Magellan radar data: Supplement to the Magellan special issue

The differences of radar data from more familiar photographic images, coupled with the unique geologic characteristics of Venus's surface, can make interpretation of the standard Magellan data products difficult for both planetary scientists and nonspecialists. We describe a set of digital processing techniques for transforming individual remote‐sensing datasets in order to make the information they contain more apparent, and for combining multiple datasets of diverse resolution and information content into synthetic images that allow the viewer to explore correlations among the data. The creation of derived data products by techniques like those we describe is becoming increasingly widespread and important, both for scientific research and for use in educational and media presentations. We apply our processing techniques to Magellan synthetic aperture radar images, altimetry, and microwave emissivity measurements of four areas that exemplify some of the most important styles of geologic activity on Venus.

Journal of Geophysical Research E: Planets↗