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At least 73 records · Page 4Linked to original sources
USGS2021

A customized framework for regional classification of conifers using automated feature extraction

Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst™, we implement our framework with 1-m 2 reference imagery provided by National Agricultural Imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods

Oregon, California, Nevada, Utah↗
USGS2015

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km × 5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.

Remote Sensing of Environment↗
USGS2020

Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as "duplicate interpretations"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.

Remote Sensing of Environment↗
USGS2021

Validation of the U.S. Geological Survey’s Land Change Monitoring, Assessment and Projection (LCMAP) collection 1.0 annual land cover products 1985–2017

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photographs, and other ancillary data. The reference sample of nearly 25,000 pixels and the accompanying 33-year time series of annual land cover reference labels allowed for a comprehensive assessment of accuracy of the LCMAP land cover and land cover change products. Overall accuracy (± standard error) for the per-pixel assessment across all years for the eight land cover classes was 82.5% (±0.2%). Overall accuracy was consistent year-to-year within a range of 1.5% but varied regionally with lower accuracy in the eastern United States. User’s accuracy (UA) and producer’s accuracy (PA) for CONUS ranged from the higher accuracies of Water (UA=96%, PA=93%) and Tree Cover (UA=90%, PA=83%) to the lower accuracies of Wetland (UA=69%, PA=74%) and Barren (UA=43%, PA=57%). For a binary change / no change classification, UA of change was 13% (±0.5%) and PA was 16% (±0.6%) for CONUS when agreement was defined as a match by the exact year of change. UA and PA improved to 28% and 34% when agreement was defined as the change being detected by the map and reference data within a ±2-year window. Change accuracy was higher in the eastern United States compared to the western US. UA was 49% (±0.3) and PA was 54% (±0.3) for the footprint of change (defined as the area experiencing at least one land cover change from 1985–2017). For class-specific loss and gain when agreement was defined as an exact year match, UA and PA were generally below 30%, with Tree Cover loss being the most accurately mapped change (UA=25%, PA=31%). These accuracy results provide users with information to assess the suitability of LCMAP data and information to guide future research for improving LCMAP products, particularly focusing on the challenges of accurately mapping annual land cover change.

Remote Sensing of Environment↗
USGS1997

An interactive program for computer-aided map design, display, and query: EMAPKGS2

EMAPKGS2 is a user-friendly, PC-based electronic mapping tool for use in hydrogeologic exploration and appraisal. EMAPKGS2 allows the analyst to construct maps interactively from data stored in a relational database, perform point-oriented spatial queries such as locating all wells within a specified radius, perform geographic overlays, and export the data to other programs for further analysis. EMAPKGS2 runs under Microsoft?? Windows??? 3.1 and compatible operating systems. EMAPKGS2 is a public domain program available from the Kansas Geological Survey. EMAPKGS2 is the centerpiece of WHEAT, the Windows-based Hydrogeologic Exploration and Appraisal Toolkit, a suite of user-friendly Microsoft?? Windows??? programs for natural resource exploration and management. The principal goals in development of WHEAT have been ease of use, hardware independence, low cost, and end-user extensibility. WHEAT'S native data format is a Microsoft?? Access?? database. WHEAT stores a feature's geographic coordinates as attributes so they can be accessed easily by the user. The WHEAT programs are designed to be used in conjunction with other Microsoft?? Windows??? software to allow the natural resource scientist to perform work easily and effectively. WHEAT and EMAPKGS have been used at several of Kansas' Groundwater Management Districts and the Kansas Geological Survey on groundwater management operations, groundwater modeling projects, and geologic exploration projects. ?? 1997 Elsevier Science Ltd.

Computers & Geosciences↗
USGS2002

Cooperative investigation of precision and accuracy in chemical analysis of silicate rocks

This is the preliminary report of the first extensive program ever organized to study the analysis of igneous rocks, a study sponsored by the United States Geological Survey, the Massachusetts Institute of Technology, and the Geophysical Laboratory of the Carnegie Institution of Washington. Large samples of two typical igneous rocks, a granite and a diabase, were carefully prepared and divided. Small samples (about 70 grams) of each were sent to 25 rock-analysis laboratories throughout the world; analyses of one or both samples were reported by 34 analysts in these laboratories. The results, which showed rather large discrepancies, are presented in histograms. The great discordance in results reflects the present unsatisfactory state of rock analysis. It is hoped that the ultimate establishment of standard samples and procedures will contribute to the improvement of quality of analyses. The two rock samples have also been thoroughly studied spectrographically and petrographically. Detailed reports of all the studies will be published.

Analytical Chemistry↗
USGS2002

Comparison of two probability distributions used to model sizes of undiscovered oil and gas accumulations: Does the tail wag the assessment?

Undiscovered oil and gas assessments are commonly reported as aggregate estimates of hydrocarbon volumes. Potential commercial value and discovery costs are, however, determined by accumulation size, so engineers, economists, decision makers, and sometimes policy analysts are most interested in projected discovery sizes. The lognormal and Pareto distributions have been used to model exploration target sizes. This note contrasts the outcomes of applying these alternative distributions to the play level assessments of the U.S. Geological Survey's 1995 National Oil and Gas Assessment. Using the same numbers of undiscovered accumulations and the same minimum, medium, and maximum size estimates, substitution of the shifted truncated lognormal distribution for the shifted truncated Pareto distribution reduced assessed undiscovered oil by 16% and gas by 15%. Nearly all of the volume differences resulted because the lognormal had fewer larger fields relative to the Pareto. The lognormal also resulted in a smaller number of small fields relative to the Pareto. For the Permian Basin case study presented here, reserve addition costs were 20% higher with the lognormal size assumption. ?? 2002 International Association for Mathematical Geology.

Mathematical Geology↗
USGS2010

Iceberg calving as a primary source of regional‐scale glacier‐generated seismicity in the St. Elias Mountains, Alaska

Since the installation of the Alaska Regional Seismic Network in the 1970s, data analysts have noted nontectonic seismic events thought to be related to glacier dynamics. While loose associations with the glaciers of the St. Elias Mountains have been made, no detailed study of the source locations has been undertaken. We performed a two-step investigation surrounding these events, beginning with manual locations that guided an automated detection and event sifting routine. Results from the manual investigation highlight characteristics of the seismic waveforms including single-peaked (narrowband) spectra, emergent onsets, lack of distinct phase arrivals, and a predominant cluster of locations near the calving termini of several neighboring tidewater glaciers. Through these locations, comparison with previous work, analyses of waveform characteristics, frequency-magnitude statistics and temporal patterns in seismicity, we suggest calving as a source for the seismicity. Statistical properties and time series analysis of the event catalog suggest a scale-invariant process that has no single or simple forcing. These results support the idea that calving is often a response to short-lived or localized stress perturbations. Our results demonstrate the utility of passive seismic instrumentation to monitor relative changes in the rate and magnitude of iceberg calving at tidewater glaciers that may be volatile or susceptible to ensuing rapid retreat, especially when existing seismic infrastructure can be used.

Alaska↗
USGS2025

Crater detection dependence on resolution, incidence angle, emission angle, and phase angle

Impact crater population detection and measurement is critical to understanding solar system bodies and dynamics. However, the ability to detect all possible craters under different lighting and camera geometries has not been systematically studied except in a few limited cases. This work presents the first systematic study examining crater detection based on resolution, incidence angle, emission angle, and phase angle, by three independent researchers to also test reliability of our conclusions. We found the best incidence angles for crater detection to be ∼74°–82°. We found little dependence on emission angle with geometries as high as ∼60°, while phase angles ∼20°–75° produce reliable crater populations. Resolution requires at least 6 pixels, and it is likely researcher-, geometry-, and terrain-specific. This work reveals potential biases in crater analysts' work and can be used for missions planning encounters so they can achieve the best, most reliable recovery of crater populations.

Geophysical Research Letters↗
USGS2020

A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States

Nearly 60,000 utility-scale wind turbines are installed in the United States as of July, 2019, representing over 97 gigawatts of electric power capacity; US wind turbine installations continue to grow at a rapid pace. Yet, until April 2018, no publicly-available, regularly updated data source existed to describe those turbines and their locations. Under a cooperative research and development agreement, analysts from three organizations collaborated to develop and release the United States Wind Turbine Database (USWTDB) - a publicly available, continuously updated, spatially rectified data source of locations and attributes of utility-scale wind turbines in the United States. Technical specifications and wind facility data, incorporated from five sources, undergo rigorous quality control. The location of each turbine is visually verified using high-resolution aerial imagery. The quarterly-updated data are available in a variety of formats, including an interactive web application, comma-separated values (CSV), shapefile, and application programming interface (API). The data are used widely by academic researchers, engineers and developers from wind energy companies, government agencies, planners, educators, and the general public.

Scientific Data↗
USGS2023

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 MWdc or more. Ground mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. Analysts from U.S. Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MW dc ) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON. Survey and Lawrence Berkeley National Laboratory collaborated to develop the United States Large-Scale Solar Photovoltaic Database (USPVDB). Using Energy Information Administration (EIA) data, locations of LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available in an interactive web application, and a number of downloadable formats, including: comma-separated value spreadsheet (CSV), application programming interface (API), and GIS shapefile.

Scientific Data↗
USGS1995

Criteria for evaluating state instream-flow programs: Deciding what works

Most states have adopted some form of instream-flow–protection program. These programs are of three types: instream-flow water rights; reservations of water for instream purposes; and conditions on consumptive water rights. No matter which type of protection program is adapted, the same question remains: How can we tell if it works? Several authors have attempted to answer this question. The works of these analysts are reviewed and criteria are suggested to evaluate the success of an instream-flow–protection program. The criteria are public confidence, certainty, proper administration, expense, and outcome in-stream.

Journal of Water Resources Planning and Management↗
USGS2001

User interface for ground-water modeling: Arcview extension

Numerical simulation for ground-water modeling often involves handling large input and output data sets. A geographic information system (GIS) provides an integrated platform to manage, analyze, and display disparate data and can greatly facilitate modeling efforts in data compilation, model calibration, and display of model parameters and results. Furthermore, GIS can be used to generate information for decision making through spatial overlay and processing of model results. Arc View is the most widely used Windows-based GIS software that provides a robust user-friendly interface to facilitate data handling and display. An extension is an add-on program to Arc View that provides additional specialized functions. An Arc View interface for the ground-water flow and transport models MODFLOW and MT3D was built as an extension for facilitating modeling. The extension includes preprocessing of spatially distributed (point, line, and polygon) data for model input and postprocessing of model output. An object database is used for linking user dialogs and model input files. The Arc View interface utilizes the capabilities of the 3D Analyst extension. Models can be automatically calibrated through the Arc View interface by external linking to such programs as PEST. The efficient pre- and postprocessing capabilities and calibration link were demonstrated for ground-water modeling in southwest Kansas.

Journal of Hydrologic Engineering↗
USGS2014

Regression models of discharge and mean velocity associated with near-median streamflow conditions in Texas: utility of the U.S. Geological Survey discharge measurement database

A database containing more than 16,300 discharge values and ancillary hydraulic attributes was assembled from summaries of discharge measurement records for 391 USGS streamflow-gauging stations (streamgauges) in Texas. Each discharge is between the 40th- and 60th-percentile daily mean streamflow as determined by period-of-record, streamgauge-specific, flow-duration curves. Each discharge therefore is assumed to represent a discharge measurement made for near-median streamflow conditions, and such conditions are conceptualized as representative of midrange to baseflow conditions in much of the state. The hydraulic attributes of each discharge measurement included concomitant cross-section flow area, water-surface top width, and reported mean velocity. Two regression equations are presented: (1) an expression for discharge and (2) an expression for mean velocity, both as functions of selected hydraulic attributes and watershed characteristics. Specifically, the discharge equation uses cross-sectional area, water-surface top width, contributing drainage area of the watershed, and mean annual precipitation of the location; the equation has an adjusted R-squared of approximately 0.95 and residual standard error of approximately 0.23 base-10 logarithm (cubic meters per second). The mean velocity equation uses discharge, water-surface top width, contributing drainage area, and mean annual precipitation; the equation has an adjusted R-squared of approximately 0.50 and residual standard error of approximately 0.087 third root (meters per second). Residual plots from both equations indicate that reliable estimates of discharge and mean velocity at ungauged stream sites are possible. Further, the relation between contributing drainage area and main-channel slope (a measure of whole-watershed slope) is depicted to aid analyst judgment of equation applicability for ungauged sites. Example applications and computations are provided and discussed within a real-world, discharge-measurement scenario, and an illustration of the development of a preliminary stage-discharge relation using the discharge equation is given.

Texas↗
USGS2010

Automated masking of cloud and cloud shadow for forest change analysis using Landsat images

Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results.

International Journal of Remote Sensing↗
USGS2019

Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields

In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.

Environmental Research Letters↗
USGS2001

Landsat-7 ETM+ radiometric calibration: Two years on-orbit

Landsat-7 has been in orbit for 2 years as of April 15, 2001 and operationally providing calibrated data products for 2 years as of June 28, 2001. A radiometric calibration team consisting of scientists and analysts from the Landsat Project Science Office, the Landsat-7 Image Assessment System and four universities evaluates the calibration based on on-board and ground-look (vicarious) calibration methodologies. The results are assembled and compared semi-annually and the calibration parameter files are adjusted as necessary. To date the combined results for the reflective bands have not shown any change from pre-launch values. The pre-launch values continue to be used for data processing, with the uncertainty estimated at less than 5%. In the thermal band, the vicarious calibration results indicated a 0.31 W/m/sup 2/ sr /spl mu/m bias in the calibration. This bias results in the ETM+ derived temperatures being about 3K high. The calibration parameter file was updated October 1, 2000 to remove this bias, however the U.S. Landsat Product Generation System (LPGS) software required modification that was not incorporated until December 20, 2000. All LPGS data products generated since this date have the correct thermal band calibration, regardless of image acquisition date, with uncertainties at approximately the 1% level.

Conference Paper↗
USGS2011

The use of historical imagery in the remediation of an urban hazardous waste site

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity.

IEEE Journal of Selected Topics in Applied Earth O↗