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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 The original 80-m-resolution Landsat Multispectral Scanner System (MSS) has now been largely superseded by the 30-m-resolution Thematic Mapper (TM) system that has additional spectral channels.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↗

USGS2011

The U.S. Geological Survey cartographic and geographic information science research activities 2006-2010

The U.S. Geological Survey (USGS) produces geospatial databases and topographic maps for the United States of America. A part of that mission includes conducting research in geographic information science (GIScience) and cartography to support mapping and improve the design, quality, delivery, and use of geospatial data and topographic maps. The Center of Excellence for Geospatial Information Science (CEGIS) was established by the USGS in January 2006 as a part of the National Geospatial Program Office. CEGIS (http://cegis.usgs.gov) evolved from a team of cartographic researchers at the Mid-Continent Mapping Center. The team became known as the Cartographic Research group and was supported by the Cooperative Topographic Mapping, Geographic Analysis and Monitoring, and Land Remote Sensing programs of the Geography Discipline of the USGS from 1999-2005. In 2006, the Cartographic Research group and its projects (http://carto-research.er.usgs.gov/) became the core of CEGIS staff and research. In 2006, CEGIS research became focused on The National Map (http://nationalmap.gov).

Cartography and Geographic Information Science↗

USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗

USGS2021

System characterization report on the Gaofen-1

Executive Summary This report addresses system characterization of Gaofen-1 and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2020. These reports present the detail methodology and procedures 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. Gaofen represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellite is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at http://www.cnsageo.com/#/detailIndex?secondIndex=2&id=3&code=8 . The Earth Resources Observation and Science Cal/Val Center of Excellence System Characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Gaofen-1 has an interior geometric performance of −0.48 meter (m) (−0.03 pixel) northing and 0.42 m (0.03 pixel) easting offset for band 1, −0.99 m (−0.06 pixel) northing and −0.38 m (−0.02 pixel) easting offset for band 2, −0.45 m (−0.03) northing and 0.83 m (0.05 pixel) easting offset for band 3, −3.20 m (−0.20 pixel) northing and 1.44 m (0.09 pixel) easting offset for band 4 in band-to-band registration. Similarly, Gaofen-1 has an exterior geometric performance of 7.50 m (0.48 pixel) easting and 109.50 m (7.30 pixels) northing offset in comparison to the Landsat 8 Operational Land Imager; a radiometric performance in the range of −0.014 to 0.149 (absolute reflective difference); and a spatial performance in the range of 1.1 to 2.0 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.040 to 0.250.

Open-File Report↗

USGS2013

Geodatabase compilation of hydrogeologic, remote sensing, and water-budget-component data for the High Plains aquifer, 2011

The High Plains aquifer underlies almost 112 million acres in the central United States. It is one of the largest aquifers in the Nation in terms of annual groundwater withdrawals and provides drinking water for 2.3 million people. The High Plains aquifer has gained national and international attention as a highly stressed groundwater supply primarily because it has been appreciably depleted in some areas. The U.S. Geological Survey has an active program to monitor the changes in groundwater levels for the High Plains aquifer and has documented substantial water-level changes since predevelopment: the High Plains Groundwater Availability Study is part of a series of regional groundwater availability studies conducted to evaluate the availability and sustainability of major aquifers across the Nation. The goals of the regional groundwater studies are to quantify current groundwater resources in an aquifer system, evaluate how these resources have changed over time, and provide tools to better understand a systems response to future demands and environmental stresses. The purpose of this report is to present selected data developed and synthesized for the High Plains aquifer as part of the High Plains Groundwater Availability Study. The High Plains Groundwater Availability Study includes the development of a water-budget-component analysis for the High Plains completed in 2011 and development of a groundwater-flow model for the northern High Plains aquifer. Both of these tasks require large amounts of data about the High Plains aquifer. Data pertaining to the High Plains aquifer were collected, synthesized, and then organized into digital data containers called geodatabases. There are 8 geodatabases, 1 file geodatabase and 7 personal geodatabases, that have been grouped in three categories: hydrogeologic data, remote sensing data, and water-budget-component data. The hydrogeologic data pertaining to the northern High Plains aquifer is included in three separate geodatabases: (1) base data from a groundwater-flow model; (2) hydrogeology and hydraulic properties data; and (3) groundwater-flow model data to be used as calibration targets. The remote sensing data for this study were developed by the U. S. Geological Survey Earth Resources Observation and Science Center and include historical and predicted land-use/land-cover data and actual evapotranspiration data by using remotely sensed temperature data. The water-budget-component data contains selected raster data from maps in the “Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 Through 1949 and 2000 Through 2009” report completed in 2011 ( http://pubs.usgs.gov/sir/2011/5183/ ). Federal Geographic Data Committee compliant metadata were created for each spatial and tabular data layer in the geodatabases.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗

USGS2024

System characterization report on the Gaofen-6

Executive Summary Gaofen-6 represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellites in this series is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium.” The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances of Gaofen-6. Results of these analyses indicate that Gaofen-6 has an interior geometric performance root mean square error ranging from 2.84 meters (m; 0.18 pixel) to 7.42 m (0.46 pixel) in easting and from 2.84 m (0.18 pixel) to 11.57 m (0.72 pixel) in northing in band-to-band registration, an exterior geometric performance root mean square error ranging from 154.50 m (8.80 pixels) in easting to 14.65 m (0.80 pixel) in northing in comparison to a corresponding Sentinel-2 scene, a radiometric performance ranging from 0.018 to 0.055 (in offset) and from 0.620 to 0.858 (in slope), and a spatial performance ranging from 2.10 to 2.30 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency ranging from 0.040 to 0.055.

Open-File Report↗

USGS2023

Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment

Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.

Maryland↗

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↗

USGS2025

Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities

Spatio-temporal changes to our world’s surface water resources are escalating. Translating how these changes impact communities and ecosystems requires time-varying data of Global Surface Water Extents (GSWE). Traditionally, GSWE mapping has been limited to static estimates, with recent efforts focusing on annual averages, frequency and occurrence of long-term variations. Building upon these foundational capabilities, we harnessed remotely sensed Sentinel-2 based near real-time Dynamic World (DW) land cover products to produce the first-of-its-kind 10 m resolution GSWE dataset representing 2015–2023. Our dataset estimated 2.5 million km 2 of permanent waters and 8 million km 2 of seasonal waters worldwide. Comparing our Sentinel-2 based data to contemporary Landsat-based GSWE, we observed that our data mapped less water within the >50% probability of occurrence range, suggesting a lower presence of open permanent water especially in high latitudes, deviating from what we previously learnt from Landsat data. Statistical analysis compared to well-established observational products and widely used GSWE datasets across some of the world’s most ecologically significant regions, including Pantanal in South America and Haor in South Asia, supports the overall physical realism of our data in predicting global open surface water dynamics. Our key contribution is a prototype Open Science operational framework that extracts routinely available DW products, runs geospatial analytics, and creates actionable water information for educators, researchers, and stakeholders at any scale of practical interest. We present examples of this operational capability through instant mapping of flood in Spain and drought in Lake Urmia, Central Asia, frequent monitoring of river extent changes at the Ganges–Brahmaputra confluence, and above all, interoperability with other existing GSWE applications.

Environmental Research Letters↗

USGS2011

Lidar vegetation mapping in national parks: Gulf Coast Network

Airborne lidar (Light Detection and Ranging) is an active remote sensing technique used to collect accurate elevation data over large areas. Lidar provides an extremely high level of regional topographic detail, which makes this technology an essential component of U.S. Geological Survey (USGS) science strategy. The USGS Coastal and Marine Geology Program (CMGP) has collaborated with the National Aeronautics and Space Administration (NASA) and the National Park Service (NPS) to acquire dense topographic lidar data in a variety of coastal environments.

Louisiana↗

USGS1996

Origins and philosophy of building a temporal database to examine human transformation processes

The U.S. Geological Survey (USGS), in partnership with the University of Maryland Baltimore County, is using historical maps and satellite images to map human-induced land transformations for the Baltimore-Washington metropolitan area. This work builds on an earlier effort that documented the historical urban development for the San Francisco Bay area. That effort used a geographic information system to compile a database that provided a visual and historical perspective of the urban growth experienced in the Bay area between 1850 and 1990. Historical overviews of urban development can be used to provide insights into the future. The Bay area work was inspired by the desire to draw upon the USGS's rich 100-year topographic map, and 20-year Landsat satellite data archives. A methodology was developed to combine the information from a variety of sources into an integrated, multi-scale, and multi-resolution dataset. Temporal urban mapping is used to examine past landscapes by incorporating historic maps, census statistics, and commerce records to generate a progressive geo-referenced representation of the past changes in the region. Contemporary analysis focuses on the use of remotely sensed data, existing digital land use data, digital census information, and a variety of Earth science infrastructure data, such as Digital Line Graphs, Digital Elevation Models, and key ancillary demographic information. The resulting database of temporal urban demographic changes provides an ideal source of test data and information for both urban geographers and global change research scientists.

San Francisco Bay↗

USGS1980

Mapping the Galilean satellites of Jupiter with Voyager data.

The four Galilean satellites of Jupiter are being mapped using image data from the Voyager 1 and 2 spacecraft. The maps are published at several scales and in several versions. Preliminary maps at 1:25,000,000-required for mission planning and preliminary science reports-were compiled within three weeks of data acquisition and have been published. Later maps incorporate Rand Corporation photogrammetric triangulations. - from Authors

Photogrammetric Engineering and Remote Sensing↗

USGS2009

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Journal of Exposure Science and Environmental Epid↗

USGS2006

The USGS Land Cover Institute

The U.S. Geological Survey (USGS) Land Cover Institute (LCI) is located at the Center for Earth Resources Observation and Science (EROS) in Sioux Falls, South Dakota. It provides a focal point for advancing USGS land cover studies and applications. Satellite images and other remotely sensed data play an important role in this research. Land Cover scientists investigate new ways to use satellite images and other data to map land cover. They assess national and global land cover characteristics and monitor how - and how rapidly - land cover changes. They also study the economic impacts of land cover as well as its effects on water quality, the spread of invasive species, habitats and biodiversity, climate variability, and other environmental factors.

Fact Sheet↗

USGS2016

Mapping presence and predicting phenological status of invasive buffelgrass in southern Arizona using MODIS, climate and citizen science observation data

The increasing spread and abundance of an invasive perennial grass, buffelgrass ( Pennisetum ciliare ), represents a critical threat to the native vegetation communities of the Sonoran desert in southern Arizona, USA, where buffelgrass eradication is a high priority for resource managers. Herbicidal treatment of buffelgrass is most effective when the vegetation is actively growing, but the remoteness of infestations and the erratic timing and length of the species’ growth periods confound effective treatment. The goal of our research is to promote buffelgrass management by using remote sensing data to detect where the invasive plants are located and when they are photosynthetically active. We integrated citizen scientist observations of buffelgrass phenology in the Tucson, Arizona area with PRISM precipitation data, eight-day composites of 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) satellite imagery, and aerially-mapped polygons of buffelgrass presence to understand dynamics and relationships between precipitation and the timing and amount of buffelgrass greenness from 2011 to 2013. Our results show that buffelgrass responds quickly to antecedent rainfall: in pixels containing buffelgrass, higher correlations (R 2 > 0.5) typically occur after two cumulative eight-day periods of rain, whereas in pixels dominated by native vegetation, four prior 8-day periods are required to reach that threshold. Using the new suite of phenometrics introduced here—Climate Landscape Response metrics—we accurately predicted the location of 49% to 55% of buffelgrass patches in Saguaro National Park. These metrics and the suggested guidelines for their use can be employed by resource managers to treat buffelgrass during optimal time periods.

Arizona↗

USGS2014

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Remote Sensing↗

USGS2022

Landsat Collection 2 Level-3 Fractional Snow Covered Area science product

The Landsat Collection 2 Level-3 Fractional Snow Covered Area science product indicates the percentage of pixels covered by snow for Landsat 4–9 imagery. Landsat’s spatial resolution offers the capability to map snow cover patterns across topographically complex mountainous regions. Snow cover is spatially and temporally variable and is often concentrated in remote or inaccessible land regions, making spaceborne remote sensing the most feasible approach to measure and monitor snow cover change.

Fact Sheet↗

USGS2007

Survey report of NOAA Ship McArthur II cruises AR-04-04, AR-05-05 and AR-06-03: habitat classification of side scan sonar imagery in support of deep-sea coral/sponge explorations at the Olympic Coast National Marine Sanctuary

Habitat mapping and characterization has been defined as a high-priority management issue for the Olympic Coast National Marine Sanctuary (OCNMS), especially for poorly known deep-sea habitats that may be sensitive to anthropogenic disturbance. As a result, a team of scientists from OCNMS, National Centers for Coastal Ocean Science (NCCOS), and other partnering institutions initiated a series of surveys to assess the distribution of deep-sea coral/sponge assemblages within the sanctuary and to look for evidence of potential anthropogenic impacts in these critical habitats. Initial results indicated that remotely delineating areas of hard bottom substrate through acoustic sensing could be a useful tool to increase the efficiency and success of subsequent ROV-based surveys of the associated deep-sea fauna. Accordingly, side scan sonar surveys were conducted in May 2004, June 2005, and April 2006 aboard the NOAA Ship McArthur II to: (1) obtain additional imagery of the seafloor for broader habitat-mapping coverage of sanctuary waters, and (2) help delineate suitable deep-sea coral-sponge habitat, in areas of both high and low commercial-fishing activities, to serve as sites for surveying-in more detail using an ROV on subsequent cruises, Several regions of the sea floor throughout the OCNMS were surveyed and mosaicked at 1-meter pixel resolution. Imagery from the side scan sonar mapping efforts was integrated with other complementary data from a towed camera sled, ROVs, sedentary samples, and bathymetry records to describe geological and biological (where possible) aspects of habitat. Using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999), we created a preliminary map of various habitat polygon features for use in a geographical information system (GIS). This report provides a description of the mapping and groundtruthing efforts as well as results of the image classification procedure for each of the areas surveyed.

Washington↗