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At least 451 records · Page 25Linked to original sources
USGS1982

Arizona Vegetation Resource Inventory (AVRI) accuracy assessment

A quantitative accuracy assessment was performed for the vegetation classification map produced as part of the Arizona Vegetation Resource Inventory (AVRI) project. This project was a cooperative effort between the Bureau of Land Management (BLM) and the Earth Resources Observation Systems (EROS) Data Center. The objective of the accuracy assessment was to estimate (with a precision of ?10 percent at the 90 percent confidence level) the comission error in each of the eight level II hierarchical vegetation cover types. A stratified two-phase (double) cluster sample was used. Phase I consisted of 160 photointerpreted plots representing clusters of Landsat pixels, and phase II consisted of ground data collection at 80 of the phase I cluster sites. Ground data were used to refine the phase I error estimates by means of a linear regression model. The classified image was stratified by assigning each 15-pixel cluster to the stratum corresponding to the dominant cover type within each cluster. This method is known as stratified plurality sampling. Overall error was estimated to be 36 percent with a standard error of 2 percent. Estimated error for individual vegetation classes ranged from a low of 10 percent ?6 percent for evergreen woodland to 81 percent ?7 percent for cropland and pasture. Total cost of the accuracy assessment was $106,950 for the one-million-hectare study area. The combination of the stratified plurality sampling (SPS) method of sample allocation with double sampling provided the desired estimates within the required precision levels. The overall accuracy results confirmed that highly accurate digital classification of vegetation is difficult to perform in semiarid environments, due largely to the sparse vegetation cover. Nevertheless, these techniques show promise for providing more accurate information than is presently available for many BLM-administered lands.

Open-File Report↗
USGS1983

Alternate data sources for soil surveys on rangeland

Soil information is an essential theme in a digital information base for land management and resource monitoring, but public land management agencies seldom have detailed soil maps available for all of the area under their administration. Most of these agencies conduct soil surveys on a scheduled basis, but escalating costs and declining budgets are reducing the number of surveys that can be scheduled. Digital elevation and satellite spectral data are available or are obtainable for all areas in the continental United States and may be used as an aid to produce soils data. A study was conducted in the Grass Creek Resource Area in north-central Wyoming to assess the utility of incorporating digital elevation and Landsat data into an information base for soil survey and to evaluate the usefulness of these data as an input to an order-three soil survey. Slope-interval maps were produced from digital elevation data and topographic maps of three 7.5-minute quadrangle areas. These slope-interval maps were then overlaid on orthophotoquadrangles and used to produce photo-interpreted physiographic maps. These physiographic maps were digitized into a data base and used with Landsat multispectral scanner data to produce tabular summaries that describe each map polygon in terms of physiographic unit, slope, aspect, elevation, area, and spectral values. A good relationship was found between the physiographic units and soil mapping units defined during a conventional soil survey, and between the Landsat spectral categories and existing vegetation communities. Field evaluations confirmed the feasibility of using this approach for producing physiographic maps as an aid for mapping soils and range sites. The project is a cooperative investigation of the Earth Resources Observation Systems Data Center of the U.S. Geological Survey, the Soil Conservation Service, and the Bureau of Land Management.

Open-File Report↗
USGS1994

Development of a digital land cover data base for the Selawik National Wildlife Refuge

Digital land cover and terrain data of the Selawik National Wildlife Refuge were produced by the U.S. Geological Survey's (USGS) Earth Resources Observation Systems Alaska Field Office for the U.S. Fish and Wildlife Service. These and other environmental data were incorporated into a Fish and Wildlife Service geographic information system to prepare a comprehensive conservation plan and an environmental impact statement for the refuge and to assist in research and management of the refuge. The digital data base, stored on tapes, includes land cover classifications from Landsat multispectral scanner data, elevation, slope, and aspect data for the area covered by the USGS Selawik and Shungnak l:250,000-scale topographic maps. The maps incorporate more than 90 percent of the refuge. Additional digital data in the data base include (a) land cover of the entire refuge at 50-, 200-, and 400-m pixel sizes derived from Landsat multispectral scanner data; (b) Landsat multispectral scanner data registered to a 50-m Universal Transverse Mercator grid; (c) elevation, slope, aspect, and solar illumination data registered to each Landsat scene; (d) stream hydrography and surficial geology digitized from USGS l:250,000-scale quadrangle maps; and (e) Refuge and wilderness boundaries. In addition to the digital products, color hard copy maps were produced from the l:250,000-scale quadrangle base land cover, elevation, slope, and aspect digital data.

Alaska↗
USGS2006

Land-Cover Trends of the Central Basin and Range Ecoregion

The U.S. Geological Survey (USGS) Land Cover Trends research project is focused on understanding the amounts, rates, trends, causes, and implications of contemporary land-use and land-cover (LU/LC) change in the United States. This project is supported by the USGS Geographic Analysis and Monitoring Program in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA). LU/LC change is a pervasive process that modifies landscape characteristics and affects a broad range of socioeconomic, biologic, and hydrologic systems. Understanding the impacts and feedbacks of LU/LC change on environmental systems requires an understanding of the rates, patterns, and driving forces of past, present, and future LU/LC change. The objectives of the Land Cover Trends project are to (1) determine and describe the amount, rates, and trends of contemporary LU/LC change by ecoregion for the period 1973-2000 for the conterminous United States, (2) document the causes, driving forces, and implications of change, and (3) synthesize individual ecoregion results into a national assessment of LU/LC change. The Land Cover Trends research team includes staff from the USGS National Center for Earth Resources Observation and Science (EROS), Rocky Mountain Geographic Science Center, Eastern Geographic Science Center, Mid-Continent Geographic Science Center, and the Western Geographic Science Center. Other partners include researchers at South Dakota State University, University of Southern Mississippi, and State University of New York College of Environmental Science and Forestry. This report presents an assessment of LU/LC change in the Central Basin and Range ecoregion for the period 1973-2000. The Central Basin and Range ecoregion is one of 84 Level-III ecoregions as defined by the Environmental Protection Agency. Ecoregions have served as a spatial framework for environmental resource management and to denote areas that contain a geographically distinct assemblage of biotic and abiotic phenomena including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The established Land Cover Trends methodology generates estimates of LU/LC change using a probability sampling approach and change-detection analysis of thematic land-cover images derived from Landsat satellite imagery.

Scientific Investigations Report↗
USGS2008

Elevation-derived watershed basins and characteristics for major rivers of the conterminous United States

The U.S. Geological Survey Earth Resources Observation and Science Center Topographic Science Project has developed elevation-derived watershed basins and characteristics for major rivers of the conterminous United States. Watershed basins are delineated upstream from the mouth of major rivers by using the hydrologic connectivity of the Elevation Derivatives for National Applications (EDNA) seamless database. Watershed characteristics are quantified by integrating ancillary geospatial datasets, including land cover, population, slope, and topography, with elevation-derived watershed boundaries. The results are published in an online EDNA Watershed Atlas at http://edna.usgs.gov/watersheds. The atlas serves as a framework for evaluating and analyzing the physical, biological, and anthropogenic status of watersheds.

Scientific Investigations Report↗
USGS2009

Comparison of Surface Flow Features from Lidar-Derived Digital Elevation Models with Historical Elevation and Hydrography Data for Minnehaha County, South Dakota

The U.S. Geological Survey (USGS) has taken the lead in the creation of a valuable remote sensing product by incorporating digital elevation models (DEMs) derived from Light Detection and Ranging (lidar) into the National Elevation Dataset (NED), the elevation layer of 'The National Map'. High-resolution lidar-derived DEMs provide the accuracy needed to systematically quantify and fully integrate surface flow including flow direction, flow accumulation, sinks, slope, and a dense drainage network. In 2008, 1-meter resolution lidar data were acquired in Minnehaha County, South Dakota. The acquisition was a collaborative effort between Minnehaha County, the city of Sioux Falls, and the USGS Earth Resources Observation and Science (EROS) Center. With the newly acquired lidar data, USGS scientists generated high-resolution DEMs and surface flow features. This report compares lidar-derived surface flow features in Minnehaha County to 30- and 10-meter elevation data previously incorporated in the NED and ancillary hydrography datasets. Surface flow features generated from lidar-derived DEMs are consistently integrated with elevation and are important in understanding surface-water movement to better detect surface-water runoff, flood inundation, and erosion. Many topographic and hydrologic applications will benefit from the increased availability of accurate, high-quality, and high-resolution surface-water data. The remotely sensed data provide topographic information and data integration capabilities needed for meeting current and future human and environmental needs.

Scientific Investigations Report↗
USGS2025

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

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

Arizona↗
USGS2024

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗
USGS2020

Calibrating geosynchronous and polar orbiting satellites: Sharing best practices

Earth remote sensing optical satellite systems are often divided into two categories—geosynchronous and sun-synchronous. Geosynchronous systems essentially rotate with the Earth and continuously observe the same region of the Earth. Sun-synchronous systems are generally in a polar orbit and view differing regions of the Earth at the same local time. Although similar in instrument design, there are enough differences in these two types of missions that often the calibration of the instruments can be substantially different. Thus, respective calibration teams develop independent methods and do not interact regularly or often. Yet, there are numerous areas of overlap and much to learn from one another. To address this issue, a panel of experts from both types of systems was convened to discover common areas of concern, areas where improvements can be made, and recommendations for the future. As a result of the panelist’s efforts, a set of eight recommendations were developed. Those that are related to improvements of current technologies include maintaining sun-synchronous orbits (not allowing orbital decay), standardization of spectral bandpasses, and expanded use of well-developed calibration techniques such as deep convective clouds, pseudo invariant calibration sites, and lunar methodologies. New techniques for expanded calibration capability include using geosynchronous instruments as transfer radiometers, continued development of ground-based prelaunch calibration technologies, expansion of RadCalNet, and development of space-based calibration radiometer systems.

Remote Sensing↗
USGS2021

Comparison of historical water temperature measurements with landsat analysis ready data provisional surface temperature estimates for the Yukon River in Alaska

Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns.

Alaska↗
USGS2023

Improving the operational simplified surface energy balance evapotranspiration model using the forcing and normalizing operation

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration ( ETa ) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration ( ETr ) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications.

Remote Sensing↗
USGS2025

Absolute radiometric calibration evaluation of Uncrewed Aerial System (UAS) Headwall and MicaSense sensors and improving data quality using the Empirical Line Method

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy.

Remote Sensing↗
USGS2012

Burn severity mapping in Australia 2009

In 2009, the Victoria Department of Sustainability and Environment estimated approximately 430,000 hectares of Victoria Australia were burned by numerous bushfires. Burned Area Emergency Response (BAER) teams from the United States were deployed to Victoria to assist local fire managers. The U.S. Geological Survey Earth Resources Observation and Science Center (USGS/EROS) and U.S. Forest Service Remote Sensing Applications Center (USFS/RSAC) aided the support effort by providing satellite-derived "soil burn severity " maps for over 280,000 burned hectares. In the United States, BAER teams are assembled to make rapid assessments of burned lands to identify potential hazards to public health and property. An early step in the assessment process is the creation of a soil burn severity map used to identify hazard areas and prioritize treatment locations. These maps are developed primarily using Landsat satellite imagery and the differenced Normalized Burn Ratio (dNBR) algorithm.

Conference Paper↗
USGS1985

Digital to Analog Conversion and Visual Evaluation of Thematic Mapper Data

As a part of the National Aeronautics and Space Administration Landsat D Image Data Quality Analysis Program, the Earth Resources Observation Systems Data Center (EDC) developed procedures to optimize the visual information content of Thematic Mapper data and evaluate the resulting photographic products by visual interpretation. A digital-to-analog transfer function was developed which would properly place the digital values on the most useable portion of a film response curve. Individual black-and-white transparencies generated using the resulting look-up tables were utilized in the production of color-composite images with varying band combinations. Four experienced photointerpreters ranked 2-cm-diameter (0. 75 inch) chips of selected image features of each band combination for ease of interpretability. A nonparametric rank-order test determined the significance of interpreter preference for the band combinations.

Journal of Imaging Technology↗
USGS2013

Metrically preserving the USGS aerial film archive

Since 1972, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, has provided fi lm-based products to the public. EROS is home to an archive of 12 million frames of analog photography ranging from 1937 to the present. The archive contains collections from both aerial and satellite platforms including programs such as the National High Altitude Program (NHAP), National Aerial Photography Program (NAPP), U.S. Antarctic Resource Center (USARC), Declass 1(CORONA, ARGON, and LANYARD), Declass 2 (KH-7 and KH-9), and Landsat (1972 – 1992, Landsat 1–5).

Photogrammetric Engineering and Remote Sensing↗
USGS2010

Appraising U.S. Geological Survey science records

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center has legislative charters to preserve and make accessible land remote sensing records important to the United States. This essay explains the appraisal process developed by EROS to ensure the science records it holds and those offered to it align with those charters. The justifications behind the questions employed to weed and to complement the EROS archive are explained along with the literature reviewed supporting their inclusion. Appraisal results are listed by individual collection and include the recommendations accepted by EROS management.

Archival Issues↗
USGS1967

Explorers from space

The statement that a new era in exploration is opening will almost surely bring to mind the venturing of man into space and the ever more imminent exploration of the moon. The reference here, however, is to exploration of earth itself and to the unique capabilities for study of the earth that space technology will provide. Demands for water, minerals, energy, food, and for working, living and recreational space are outrunning our ability to meet them by traditional methods. In order to satisfy these demands, it is necessary now, just as it has been in the past, to look to the activities, the instruments, and the technologies that in part create the pressures for aid in meeting them. Studies being made at the U.S. Geological Survey and elsewhere of the potential applications of remote sensors in space to earth resources research indicate that now, at last, it will be possible to approach solutions on a regional or global basis. This paper discusses the plans for an Earth Resources Observational Satellites Program which will be designed for that purpose.

Journal of Geological Education↗
USGS1977

Urban and regional land use change detected by using Landsat data

The Atlanta Regional Commission and the Earth Resources Observation Systems Data Center participated in a demonstration of the use of Landsat digital data to detect land use change in the Atlanta, Ga., area. Temporal overlays combining Landsat band-5 data from October 1972 and 1974 were made by using the General Electric Image 100 system. The 1972 data were divided by the 1974 data, and low ratios indicate areas where land use and land cover had changed. These low ratios were classified into a land-use-change theme. A classification based on the use of the four bands of the multispectral scanner data, taken in 1974, yielded six land use categories: (1) Commercial-industrial-multifamily, (2) single-family residential, (3) cleared land, (4) open space, (5) forested land, and (6) water. These six 1974 categories and the change theme were combined to determine changes between 1972 and 1974.

Georgia↗