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

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At least 685 records · Page 38Linked to original sources

USGS1988

Intermediate-scale vegetation mapping of Innoko National Wildlife Refuge, Alaska using Landsat MSS digital data

A Landsat-derived vegetation map was prepared for lnnoko National Wildlife Refuge. The refuge lies within the northern boreal subzone of northwestern central Alaska. Six major vegetation classes and 21 subclasses were recognized: forest (closed needleleaf, open needleleaf, needleleaf woodland, mixed, and broadleaf); broadleaf scrub (lowland, upland burn regeneration, subalpine); dwarf scrub (prostrate dwarf shrub tundra, erect dwarf shrub heath, dwarf shrub-graminoid peatland, dwarf shrub-graminoid tussock peatland, dwarf shrub raised bog with scattered trees, dwarf shrub-graminoid marsh); herbaceous (graminoid bog, graminoid marsh, graminoid tussock-dwarf shrub peatland); scarcely vegetated areas (scarcely vegetated scree and floodplain); and water (clear, sedimented). The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photo-interpretation, and digital Landsat data. Major steps in the Landsat analysis involved preprocessing (geometric correction), derivation of statistical parameters for spectral classes, spectral class labeling of sample areas, preliminary classification of the entire study area using a maximum-likelihood algorithm, and final classification utilizing ancillary information such as digital elevation data. The final product is 1:250,000-scale vegetation map representative of distinctive regional patterns and suitable for use in comprehensive conservation planning.

Alaska↗

USGS2014

Landsat: Sustaining earth observations beyond Landsat 8

The Landsat series of Earth-observing satellites began 41-years ago as a partnership between the U.S. Geological Survey (USGS) of the Department of the Interior (DOI) and The National Aeronautics and Space Administration (NASA). For the past 41 years, Landsat satellites and associated U.S. Government ground processing, distribution, and archiving systems have acquired and made available global, moderate-resolution, multispectral measurements of land and coastal regions, providing humankind’s longest record of our planet from space. Landsat information is truly a national asset, providing an important and unique capability that benefits abroad community, including Federal, state, and local governments; globalchange science; academia, and the private sector.

Photogrammetric Engineering and Remote Sensing↗

USGS2016

Normalized burn ratios link fire severity with patterns of avian occurrence

Context Remotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains. Objectives We evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire. Methods We identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales. Results Agreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire. Conclusions DNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

Landscape Ecology↗

USGS2019

Digital mapping of ecological land units using a nationally scalable modeling framework

Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the United States. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here, we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based on the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western United States, representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with 10-fold cross-validation accuracies of 70% in the Colorado Plateau based on 1405 point observations across eight expertly-defined ESG classes and 79% in the Chihuahuan Desert based on 2589 point observations across nine expertly-defined ESG classes. Model accuracies were also evaluated using external-validation datasets; resulting in 56 and 44% correct classification for the Colorado Plateau and Chihuahuan Desert, respectively. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.

Chihuahuan Desert, Colorado Plateau↗

USGS2014

Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation

There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, Typha spp. and Schoenoplectus acutus , at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R 2 = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R 2 = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species ( Typha spp.: %RMSE = 18.5%; S. acutus : %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R 2 = 0.56, %RMSE = 20.9%, compared to R 2 = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R 2 = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.

California↗

USGS2021

Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50-100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in-situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters ( e.g. friction coefficient, bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.

Water Resources Research↗

USGS1992

Maps showing mineral resource assessment for skarn deposits of gold, silver, copper, tungsten, and iron in the Butte 1 degree by 2 degrees Quadrangle, Montana

The purpose of this report is to assess the potential for undiscovered skarn deposits of gold, silver, copper, tungsten, and iron in the Butte 1 °X2° quadrangle. Other deposit types have been assessed and reports for each of the following have been prepared: Vein and replacement deposits of gold, silver, copper, lead, zinc, ·manganese, and tungsten; porphyry-stockwork deposits of copper, molybdenum, and tungsten; stockwork-disseminated deposits of gold and silver; placer deposits of gold; and miscellaneous deposit types including strata-bound deposits of copper and silver in rocks of the Middle Proterozoic Belt Supergroup, phosphate deposits in the Permian Phosporia Formation, and deposits of barite and fluorite. The Butte quadrangle, in west-central Montana, is one of the most mineralized and productive mining regions in the U.S. Its mining districts, including the world famous Butte or Summit Valley district, have produced a variety of metallic and nonmetallic mineral commodities valued at more than $6.4 billion (at the time of production). Because of its importance as a mineral producing region, the Butte quadrangle was selected for study by the U.S. Geological Survey under the Conterminous United States Mineral Assessment Program (CUSMAP). Under this program, new data on geology, geochemistry, geophysics, geochronology, mineral resources, and remote sensing were collected and synthesized. The field and laboratory studies were supported, in part, by funding from the Geologic Framework and Synthesis Program and the Wilderness Program. The methods used in resource assessment include a compilation of all data into data sets, the development of an occurrence model for skarn deposits in the quadrangle, and the analysis of data using techniques provided by a Geographic Information System (GIS). This map is one of a number of reports and maps on the Butte 1 °X2° quadrangle. Other publications resulting from this study include U.S. Geological Survey (USGS) Miscellaneous Investigations Series Maps 1-2050-A (Rowan and Segal, 1989), 1-2050-B (Rowan and others, 1991), 1-2050-D (Elliott and others, in press); Miscellaneous Field Studies Map MF-1925 (Wallace, 1987a); and Open-File Reports OF-86-292 (Wallace and others, 1986) and OF-86-0632 (Elliott and others, 1986).

IMAP↗

USGS1986

Vegetation mapping of Nowitna National Wildlife Refuge, Alaska using Landsat MSS digital data

A Landsat-derived vegetation map was prepared for Nowitna National Wildlife Refuge. The refuge lies within the middle boreal subzone of north central Alaska. Seven major vegetation classes and sixteen subclasses were recognized: forest (closed needleleaf, open needleleaf, needleleaf woodland, mixed, and broadleaf); broadleaf scrub (lowland, alluvial, subalpine); dwarf scrub (prostrate dwarf shrub tundra, dwarf shrub-graminoid tussock peatland); herbaceous (graminoid bog, marsh and meadow); scarcely vegetated areas (scarcely vegetated scree and floodplain); water (clear, turbid); and other areas (mountain shadow). The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photointerpretation, and digital Landsat data. Major steps in the Landsat analysis involved preprocessing (geometric correction), derivation of statistical parameters for spectral classes, spectral class labeling of sample areas, preliminary classification of the entire study area using a maximum-likelihood algorithm, and final classification utilizing ancillary information such as digital elevation data. The final product is a 1:250,000-scale vegetation map representative of distinctive regional patterns and suitable for use in comprehensive conservation planning.

Alaska↗

USGS2024

Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems

The deposits from volcanic eruptions represent the record of activity at a volcano. Identification, classification, and interpretation of these deposits are crucial to the understanding of volcanic processes and assessing hazards. However, deposits often cover large areas and can be difficult or dangerous to access, making field mapping dangerous and time-consuming. Remote sensing techniques are often used to map and identify the deposits of volcanic eruptions, though these techniques present their own trade-offs in terms of image resolution, wavelength, and observation frequency. Here, we present a new approach for mapping and classifying volcanic deposits using a multi-sensor unoccupied aerial system (UAS), and demonstrate its application on lava and tephra deposits associated with the 2018 eruption of Sierra Negra volcano (Galápagos Archipelago, Ecuador). We surveyed the study area and collected visible and thermal infrared (TIR) images. We used structure-from-motion photogrammetry to create a digital elevation model (DEM) from the visual images and calculated the solar heating rate of the surface from temperature maps based on the TIR images. We find that the solar heating rate is highest for tephra deposits and lowest for ʻaʻā lava, with pāhoehoe lava having intermediate values. This is consistent with the solar heating rate correlating to the density and particle size of the surface. The solar heating rate for the lava flow also decreases with increasing distance from the vent, consistent with an increase in density as the lava degasses. We combined the surface roughness (calculated from the DEM) and the solar heating rate of the surface to remotely classify tephra deposits and different lava morphologies. We applied both supervised and unsupervised machine learning algorithms and demonstrate that supervised methods can replicate the manual classification while the unsupervised method can identify major surface units with no ground truth information. These methods allow for remote mapping and classification at high spatial resolution (< 1 meter) of a variety of volcanic deposits, with potential for application to deposits from other processes (e.g., fluvial, glacial) and deposits on other planetary bodies.

Galápagos Archipelago, Sierra Negra volcano↗

USGS2019

Bundle adjustment using space based triangulation method for improving the Landsat global ground reference

There is an ever-increasing interest and need for accurate geo-registration of remotely sensed data products to a common global geometric reference. Although the geo-registration has improved significantly in the last decade, the lack of an accurate global ground reference dataset poses serious issues for data providers seeking to make geometrically stackable analysis ready data. The existing Global Land Survey 2000 (GLS2000) dataset derived from Landsat 7 images provide global coverage and can be used as a reference dataset, but its accuracy is much lower than what can be attained using the agile and precise pointing capability of the new spacecrafts. The improved position and pointing knowledge of the new spacecrafts such as Landsat 8 can be used to improve the accuracy of the existing global ground control points using a space based triangulation method. This paper discusses the theoretical basis, formulation, and application of the space based triangulation method at a continental scale to improve the accuracy of the GLS-derived ground control points.Our triangulation method involves adjusting the spacecraft position, velocity, attitude, attitude rate, and ground control point locations, iteratively, by linearizing the non-linear viewing geometry, such that the residual errors in the measured image points are minimized. The complexity of the numerical inversion and processing is dealt with in our approach by processing and eliminating the ground points one at a time. This helps to reduce the size of the normal matrix significantly, thereby making the triangulation of a continent-wide scale block feasible and efficient. One of the unique characteristics of our method is the use of a correlation model linking the attitude corrections between images of the same pass, which promotes consistency in the attitude corrections. We evaluated the performance of our triangulation method over the Australian continent using the Australian Geographic Reference Image (AGRI) dataset as a reference. Both a free adjustment, using only the pointing information of the Landsat 8 spacecraft, and a constrained adjustment, using the AGRI as external control were performed and the results compared. The Australian block’s horizontal accuracy improved from 15.4 m to 3.6 m with the use of AGRI controls, and from 15.4 m to 8.8 m without the use of AGRI controls.

Remote Sensing↗

USGS2020

Tracking rates of postfire conifer regeneration vs. deciduous vegetation recovery across the western United States

Postfire shifts in vegetation composition will have broad ecological impacts. However, information characterizing postfire recovery patterns and their drivers are lacking over large spatial extents. In this analysis, we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of postfire, dual-season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally specific drivers of postfire rates of NDVI recovery. Rates of postfire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data ( n = 230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in postfire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track postfire forest recovery across the western United States. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Ecological Applications↗

USGS2015

Long-term effects of wildfire on greater sage-grouse - integrating population and ecosystem concepts for management in the Great Basin

Greater sage-grouse ( Centrocercus urophasianus ; hereinafter, sage-grouse) are a sagebrush obligate species that has declined concomitantly with the loss and fragmentation of sagebrush ecosystems across most of its geographical range. The species currently is listed as a candidate for federal protection under the Endangered Species Act (ESA). Increasing wildfire frequency and changing climate frequently are identified as two environmental drivers that contribute to the decline of sage-grouse populations, yet few studies have rigorously quantified their effects on sage-grouse populations across broad spatial scales and long time periods. To help inform a threat assessment within the Great Basin for listing sage-grouse in 2015 under the ESA, we conducted an extensive analysis of wildfire and climatic effects on sage-grouse population growth derived from 30 years of lek-count data collected across the hydrographic Great Basin of Western North America. Annual (1984–2013) patterns of wildfire were derived from an extensive dataset of remotely sensed 30-meter imagery and precipitation derived from locally downscaled spatially explicit data. In the sagebrush ecosystem, underlying soil conditions also contribute strongly to variation in resilience to disturbance and resistance to plant community changes (R&R). Thus, we developed predictions from models of post-wildfire recovery and chronic effects of wildfire based on three spatially explicit R&R classes derived from soil moisture and temperature regimes. We found evidence of an interaction between the effects of wildfire (chronically affected burned area within 5 kilometers of a lek) and climatic conditions (spring through fall precipitation) after accounting for a consistent density-dependent effect. Specifically, burned areas near leks nullifies population growth that normally follows years with relatively high precipitation. In models, this effect results in long-term population declines for sage-grouse despite cyclic periods of high precipitation. Based on 30-year projections of burn and recovery rates, our population model predicted steady and substantial long-term declines in population size across the Great Basin. Further, example management scenarios that may help offset adverse wildfire effects are provided by models of varying levels of fire suppression and post-wildfire restoration that focus on areas especially important to sage-grouse populations. These models illustrate how sage-grouse population persistence likely will be compromised as sagebrush ecosystems and sage-grouse habitat are degraded by wildfire, especially in a warmer and drier climate, and by invasion of annual grasses that can increase wildfire frequency and size in the Great Basin.

California, Idaho, Nevada, Oregon, Utah↗

USGS2014

Geologic map of Mars

This global geologic map of Mars, which records the distribution of geologic units and landforms on the planet's surface through time, is based on unprecedented variety, quality, and quantity of remotely sensed data acquired since the Viking Orbiters. These data have provided morphologic, topographic, spectral, thermophysical, radar sounding, and other observations for integration, analysis, and interpretation in support of geologic mapping. In particular, the precise topographic mapping now available has enabled consistent morphologic portrayal of the surface for global mapping (whereas previously used visual-range image bases were less effective, because they combined morphologic and albedo information and, locally, atmospheric haze). Also, thermal infrared image bases used for this map tended to be less affected by atmospheric haze and thus are reliable for analysis of surface morphology and texture at even higher resolution than the topographic products.

Scientific Investigations Map↗

USGS2010

Technical note assessing the accuracy of Landsat Thematic Mapper classification using double sampling

Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Mountain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5 per cent and 32.5 per cent, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6 per cent and 45.6 per cent, respectively.

International Journal of Remote Sensing↗

USGS1993

Twenty years of Landsat data accessible through the national satellite land remote sensing data archive

The EROS Data Center has managed to National Satellite Land Remote Sensing Data Archive's (NSLRSDA) Landsat data since 1972. The NSLRSDA includes Landsat MSS data from 1972 through 1991 and T M data from 1982 through 1993. In response to many requests from multi-disciplined users for an enhanced insight into the availability and volume of Landsat data over specific worldwide land areas, numerous world plots and corresponding statical overviews have been prepared. These presentations include information related to image quality, cloud cover, various types of data overage (i.e. regions, countries, path, rows), acquisition station coverage areas, various archive media formats (i.e. wide band video tapes, computer compatible tapes, high density tapes, etc.) and acquisition time periods (i.e. years, seasons). Plans are to publish this information in a paper sample booklet at the Pecora 12 Symposium, in a USGS circular and on a Landsat CD-ROM; the data will be also be incorporated into GLIS.

Pecora 12 Symposium↗

USGS2025

The ghost plume phenomenon and its impact on zenith-facing remote sensing measurements of volcanic SO2 emission rates

A large source of error in SO 2 emission rates derived from mobile Differential Optical Absorption Spectroscopy (DOAS) of volcanic gas plumes is the uncertainty in atmospheric light paths between the sun and the instrument, particularly under non-ideal atmospheric conditions, such as the presence of low clouds. DOAS instruments measure the SO 2 column density along the effective light path, so changes to that pathway directly affect the measured SO 2 signal. Due to complex radiative transfer mechanisms when a cloud is between the DOAS viewing position and a volcanic plume, measured plumes can appear spatially offset from their true location, a phenomenon informally referred to as “ghost plumes.” In addition to the appearance of ghost plumes, DOAS measurements recorded in non-ideal conditions have poorly characterized errors and are often discarded, limiting the data available to characterize volcanic degassing. In this study we simulate the radiative transfer associated with zenith-facing mobile DOAS traverses using the McArtim radiative transfer model for scenarios when there is a cloud layer between the instrument and the volcanic plume. In total, 217 permutations of atmospheric optical conditions are considered with varying cloud opacities (AOD = 0, 1, 2, 4, 8, 20), plume opacities (AOD = 0, 1, 2, 4, 8), solar zenith angles (SZA = 1°, 30°, 60°), and cloud thicknesses (200, 400, 800 m). We first develop objective criteria for selecting SO 2 baseline absorption levels and plume spatial extents. The simulated plume traverses are then integrated to obtain the SO 2 cross-sectional burdens which, after multiplication with the wind speed, yield SO 2 emission rates. We find large modification in the shape of the modeled cross-sectional burdens even under translucent (low AOD) cloud conditions in our modeled scenarios. Despite modification of the plume shape, the presence of a low cloud layer is typically not a large source of error in the SO 2 cross-sectional burden or emission rate obtained from zenith-facing DOAS traverses. We find that all measured cross-sectional burdens simulated using an aerosol-free plume in the above conditions and SZA ≤ 30° are within ±25% of the true value.

Journal of Volcanology and Goethermal Research↗

USGS2023

Ensemble estimation of historical evapotranspiration for the conterminous U.S.

Evapotranspiration (ET) is the largest component of the water budget, accounting for the majority of the water available from precipitation. ET is challenging to quantify because of the uncertainties associated with the many ET equations currently in use, and because observations of ET are uncertain and sparse. In this study, we combine information provided by available ET data and equations to produce a new monthly data set for ET for the conterminous U.S. (CONUS). These maps are produced from 1895 to 2018 at an 800 m spatial scale, marking a finer resolution than currently available products over this time period. In our approach, the relative performance of a suite of ET equations is assessed using water balance, flux tower, and remotely sensed ET estimates. At the observation locations, we use error distributions to quantify relative weights for the equations and use these in a modified Bayesian model averaging weighted ensemble approach. The relative weights are spatially generalized using a random forest regression, which is applied to wall-to-wall explanatory variable maps to generate CONUS-wide relative weight maps and ensemble estimates. We assess the performance of the ensemble using a reserved subset of the observations and compare this performance against other national-scale map products for historical to modern ET. The ensemble ET maps are shown to provide an improved accuracy over the alternative comparison products. These ET maps could be useful for a variety of hydrologic modeling and assessment applications that benefit from a long record, such as the study of periods of water scarcity through time.

Water Resources Research↗

USGS2012

Generation of a U.S. national urban land use product

Characterization of urban land uses is essential for many applications. However, differentiating among thematically-detailed urban land uses (residential, commercial, industrial, institutional, recreational, etc.) over broad areas is challenging, in part because image-based solutions are not ideal for establishing the contextual basis for identifying economic function and use. At present no current United States national-scale mapping exists for urban land uses similar to the classical Anderson Level II classification. This paper describes a product that maps urban land uses, and is linked to and corresponds with the National Land Cover Database (NLCD) 2006. In this product, NLCD urban pixels, in addition to their current imperviousness intensity classification, are assigned one of nine urban use classes based on information drawn from multiple data sources. These sources include detailed infrastructure information, population characteristics, and historical land use. The result is a method for creating a 30 m national-scale grid providing thematically-detailed urban land use information which complements the NLCD. Initial results for 10 major metropolitan areas are provided as an on-line link. Accuracy assessment of initial products yielded an overall accuracy of 81.6 percent.

Photogrammetric Engineering and Remote Sensing↗