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At least 1,135 records · Page 63Linked to original sources

USGS2015

Geographic Information Systems (GIS) for la République Islamique de Mauritanie (PRISM-II) phase V (phase V, deliverable 92)

In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector. To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania. This report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.

Open-File Report↗

USGS2026

Predicting Minnesota lake ice phenology with deep learning, explainable methods, and a physically based benchmark, 1980-2018

Globally, lakes are losing ice cover, but our understanding of the rates and variability of change are biased toward few lakes with long-term records. Recent works have used remote sensing and predictive modeling to supplement the observational record, but these approaches produce large errors when estimating ice phenology for individual lakes. Accurate, lake-specific estimates of ice formation and breakup through time are important for estimating variability in ice loss across heterogenous lakes and for understanding broader implications. This work explores machine learning (ML) approaches for hindcasting ice phenology using observations from 1980 to 2018 across 625 Minnesota lakes, covering 4359 lake-years of record. We used daily weather and static lake attributes to develop 60 neural networks for hindcasting lake ice time series. We considered LSTMs and attention-based transformers of varying size and initial parameters. We found that the largest LSTM performed most accurately on withheld data, and on a test set of unseen years and lakes, it outperformed a state-of-the-art physically based model in daily accuracy (97% vs. 95%), year-level metrics (RMSE for ice formation = 6.9 vs. 13.2 days; ice breakup = 7.5 vs. 12.2 days; ice duration = 9.6 vs. 13.7 days), and estimating loss of ice cover from 1980 to 2018 (observed = 8.4 days, LSTM = 7.2 days, GLM = 3.4 days). This demonstrates that ML can estimate historical lake ice formation and breakup dates within a week of observed phenology across heterogeneous lakes and recreate broad scale trends in ice cover phenology with limited long-term ice records.

Minnesota↗

USGS2016

Remote estimation of surface pCO2 on the West Florida Shelf

Surface p CO 2 data from the West Florida Shelf (WFS) have been collected during 25 cruise surveys between 2003 and 2012. The data were scaled up using remote sensing measurements of surface water properties in order to provide a more nearly synoptic map of p CO 2 spatial distributions and describe their temporal variations. This investigation involved extensive tests of various model forms through parsimony and Principal Component Analysis, which led to the development of a multi-variable empirical surface p CO 2 model based on concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) estimates of surface chlorophyll a concentrations (CHL, mg m −3 ), diffuse light attenuation at 490 nm (Kd_Lee, m −1 ), and sea surface temperature (SST, °C). Validation using an independent dataset showed a p CO 2 Root Mean Square Error (RMSE) of <12 µatm and a 0.88 coefficient of determination (R 2 ) for measured and model-predicted p CO 2 ranging from 300 to 550 µatm. The model was more sensitive to SST than to CHL and Kd_Lee, with a 1 °C change in SST leading to a ~16 µatm change in the predicted p CO 2 . Application of the model to the entire WFS MODIS time series between 2002 and 2014 showed clear seasonality, with maxima (~450 µatm) in summer and minima (~350 µatm) in winter. The seasonality was positively correlated to SST (high in summer and low in winter) and negatively correlated to CHL and Kd_Lee (high in winter and low in summer). Inter-annual variations of p CO 2 were consistent with inter-annual variations of SST, CHL, and Kd_Lee. These results suggest that surface water p CO 2 of the WFS can be estimated, with known uncertainties, from remote sensing. However, while the general approach of empirical regression may work for waters from other areas of the Gulf of Mexico, model coefficients need to be empirically determined in a similar fashion.

Continental Shelf Research↗

USGS2015

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future

Sagebrush ( Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km 2 (1.1%) across the study area under the A1B scenario and 41.15 km 2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km 2 (4.1%) under A1B and 50.8 km 2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km 2 (3.8%) under A1B and 40.59 km 2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse ( Centrocercus urophasianus ) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.

Wyoming↗

USGS2021

Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak ( R 2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger ( R 2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

Alaska↗

USGS2010

Accuracy of gap analysis habitat models in predicting physical features for wildlife-habitat associations in the southwest U.S.

Despite widespread and long-standing efforts to model wildlife-habitat associations using remotely sensed and other spatially explicit data, there are relatively few evaluations of the performance of variables included in predictive models relative to actual features on the landscape. As part of the National Gap Analysis Program, we specifically examined physical site features at randomly selected sample locations in the Southwestern U.S. to assess degree of concordance with predicted features used in modeling vertebrate habitat distribution. Our analysis considered hypotheses about relative accuracy with respect to 30 vertebrate species selected to represent the spectrum of habitat generalist to specialist and categorization of site by relative degree of conservation emphasis accorded to the site. Overall comparison of 19 variables observed at 382 sample sites indicated ???60% concordance for 12 variables. Directly measured or observed variables (slope, soil composition, rock outcrop) generally displayed high concordance, while variables that required judgments regarding descriptive categories (aspect, ecological system, landform) were less concordant. There were no differences detected in concordance among taxa groups, degree of specialization or generalization of selected taxa, or land conservation categorization of sample sites with respect to all sites. We found no support for the hypothesis that accuracy of habitat models is inversely related to degree of taxa specialization when model features for a habitat specialist could be more difficult to represent spatially. Likewise, we did not find support for the hypothesis that physical features will be predicted with higher accuracy on lands with greater dedication to biodiversity conservation than on other lands because of relative differences regarding available information. Accuracy generally was similar (>60%) to that observed for land cover mapping at the ecological system level. These patterns demonstrate resilience of gap analysis deductive model processes to the type of remotely sensed or interpreted data used in habitat feature predictions. ?? 2010 Elsevier B.V.

Ecological Modelling↗

USGS2024

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High-resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double-counted plants) error in counts from high-resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post-wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high-resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting ( p ) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Methods in Ecology and Evolution↗

USGS2022

The Geodetic Centroid (gCent) Catalog: Global earthquake monitoring with satellite imaging geodesy

Remote sensing geodetic observations (Interferometric Synthetic Aperture Radar [InSAR] and optical correlation [“pixel tracking”]) serve an increasingly diverse and important role in earthquake monitoring and response. This study introduces the Geodetic Centroid (gCent) catalog—an earthquake catalog derived solely from space‐based geodetic observations—and analysis of 74 earthquakes ( ⁠ M W 4.3–7.4) imaged from 1 August 2019 to 01 February 2022. For gCent, we use InSAR and optical correlation observations derived from the Sentinel‐1 satellites and various publicly available optical satellites to systematically image all global earthquakes M W 5.5 or larger and shallower than 25 km, M W 7.0 or larger at any depth, and other high‐impact earthquakes or seismic events of special interest. We invert surface displacements from successfully imaged earthquakes for the location, orientation, and dimensions of a single slipping fault patch that describes the centroid characteristics of the earthquake. These centroid models, in turn, are compiled into a catalog and used in U.S. Geological Survey/Advanced National Seismic System (ANSS) operational earthquake response products such as ShakeMaps and finite‐fault models. We provide a comparison of the gCent catalog to the ANSS Comprehensive Catalog and Global Centroid Moment Tensor (Global CMT) catalog to compare reported locations, depths, and magnitudes. We find that global earthquake catalogs not only generally provide reasonably comparable locations (within 10 km on average), but also they systematically overestimate depth that may have implications for earthquake shaking predictions based solely on earthquake origin information. Geodetic magnitudes are comparable to seismically inferred magnitudes, indicating that gCent models are unlikely to be systematically biased by the presence of postseismic deformation. We additionally highlight limitations of the gCent catalog induced by both the limitations of remote sensing imaging of earthquakes and our imposition of a simplified earthquake source description that does not include spatially distributed slip.

Bulletin of the Seismological Society of America↗

USGS2010

Conifer health classification for Colorado, 2008

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Scientific Investigations Map↗

USGS2016

Semi-arid vegetation response to antecedent climate and water balance windows

Questions Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation? Location Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years. Results Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology. Conclusions We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.

Applied Vegetation Science↗

USGS2016

Benchmarking wide swath altimetry-based river discharge estimation algorithms for the Ganges river system

The objective of this study is to compare the effectiveness of three algorithms that estimate discharge from remotely sensed observables (river width, water surface height, and water surface slope) in anticipation of the forthcoming NASA/CNES Surface Water and Ocean Topography (SWOT) mission. SWOT promises to provide these measurements simultaneously, and the river discharge algorithms included here are designed to work with these data. Two algorithms were built around Manning's equation, the Metropolis Manning (MetroMan) method, and the Mean Flow and Geomorphology (MFG) method, and one approach uses hydraulic geometry to estimate discharge, the at-many-stations hydraulic geometry (AMHG) method. A well-calibrated and ground-truthed hydrodynamic model of the Ganges river system (HEC-RAS) was used as reference for three rivers from the Ganges River Delta: the main stem of Ganges, the Arial-Khan, and the Mohananda Rivers. The high seasonal variability of these rivers due to the Monsoon presented a unique opportunity to thoroughly assess the discharge algorithms in light of typical monsoon regime rivers. It was found that the MFG method provides the most accurate discharge estimations in most cases, with an average relative root-mean-squared error (RRMSE) across all three reaches of 35.5%. It is followed closely by the Metropolis Manning algorithm, with an average RRMSE of 51.5%. However, the MFG method's reliance on knowledge of prior river discharge limits its application on ungauged rivers. In terms of input data requirement at ungauged regions with no prior records, the Metropolis Manning algorithm provides a more practical alternative over a region that is lacking in historical observations as the algorithm requires less ancillary data. The AMHG algorithm, while requiring the least prior river data, provided the least accurate discharge measurements with an average wet and dry season RRMSE of 79.8% and 119.1%, respectively, across all rivers studied. This poor performance is directly traced to poor estimation of AMHG via a remotely sensed proxy, and results improve commensurate with MFG and MetroMan when prior AMHG information is given to the method. Therefore, we cannot recommend use of AMHG without inclusion of this prior information, at least for the studied rivers. The dry season discharge (within-bank flow) was captured well by all methods, while the wet season (floodplain flow) appeared more challenging. The picture that emerges from this study is that a multialgorithm approach may be appropriate during flood inundation periods in Ganges Delta.

Ganges River Delta↗

USGS2024

Investigation of land cover within wetland complexes at Dixie Meadows, Churchill County, Nevada, from October 2015 to January 2022

The U.S. Geological Survey investigated land cover at subannual time steps within six wetland areas in Dixie Valley, Churchill County, Nevada, from October 2015 to January 2022. As requested by the U.S. Fish and Wildlife Service, we used aerial photography and satellite remote sensing data to map surface water and other land cover types within the wetland complexes. We identified five land cover classes using the green normalized difference vegetation index (gNDVI) and its inverse relationship to the normalized difference water index (NDWI) within three U.S. Department of Agriculture National Agriculture Imagery Program aerial images (acquired in 2015, 2017, and 2019) and 110 European Space Agency Sentinel-2 satellite images (acquired 2015–2022). The relative wetness of soil conditions within each land cover class is estimated by comparison to previously published observations of relative conductivity measured by 79 field-based sensors within the wetlands from 2019 to 2021. We mapped the areal coverage of the five land cover classes for approximately 385 acres (1,559,000 square meters [m²]) comprising six individual wetland complexes as well as a larger 1,298- acre (5,254,000-m 2 ) area of interest inclusive of the wetland complexes and adjacent landscape. Land cover of open water (Class 5) primarily within ponds at one of the wetland complexes comprised 8,333 m 2 , on average, of the wetland complexes. Land cover of mixed shallow surface water, saturated soil, and vegetation (Class 4) comprised 111,723 m 2 on average of the wetland complexes. Land cover of dense green vegetation canopy cover (Class 3) that often (46 percent of observations) had underlying surface water or saturated soil conditions comprised 592,522 m 2 on average of the wetland complexes. The remaining areas of the wetland complexes not mapped as these three land cover types (Classes 2 and 1) had sparse vegetation or bare soil cover and commonly (greater than or equal to 67 percent of observations) had dry soil conditions. The investigation of land cover detailed in this report could inform future efforts to map land cover more precisely via higher resolution remote sensing or ground-based surveying or could be incorporated with other environmental monitoring data to characterize habitat and hydrology of the wetland complexes at Dixie Meadows.

Nevada↗

USGS2014

Porphyry copper assessment of western Central Asia

The U.S. Geological Survey conducted an assessment of resources associated with porphyry copper deposits in the western Central Asia countries of Kyrgyzstan, Uzbekistan, Kazakhstan, and Tajikistan and the southern Urals of Kazakhstan and Russia as part of a global mineral resource assessment. The purpose of the study was to (1) delineate permissive areas (tracts) for undiscovered porphyry copper deposits; (2) compile a database of known porphyry copper deposits and significant prospects; (3) where data permit, estimate numbers of undiscovered deposits within those permissive tracts; and (4) provide probabilistic estimates the amounts of copper (Cu), molybdenum (Mo), gold (Au), and silver (Ag) that could be contained in those undiscovered deposits. Western Central Asia, a region diverse in its geologic complexity, is situated north of the Tarim and North China tectonic blocks and sandwiched between the East European and Siberian cratons. The Ural Mountains form the western margin of the region; the southern margin is formed by the high-standing ranges that make up the Tian Shan mountain range in the border regions of Kazakhstan, Kyrgyzstan, and western China, where the effects of collisional tectonics are well displayed. The tectonic collage that makes up the core of western Central Asia is perhaps the geologically least understood part of the region. There is broad agreement that the early Paleozoic is made up of tectonically juxtaposed blocks that vary from Precambrian-cored microcontinents to magmatic arc and related complexes to subduction-related accretionary complexes. The rudiments of an incipient, contiguous single Kazakhstan block were formed by the end of the Silurian. In the middle to late Paleozoic, the block was unconformably superposed by two large, nested magmatic-arc belts, one Devonian, the other Carboniferous. Both magmatic-arc complexes were folded into a horseshoe-shaped, southeast-opening orocline in response to the final collisions of the various surrounding cratonic blocks with the Kazakhstan block. Additional deformation in the upper Cenozoic derived from the collision of India and China significantly redistributed fragments of the various mosaicked blocks, particularly in the central and southern parts of the western Central Asian region. Porphyry copper deposits are associated with many of the magmatic-arc fragments and belts throughout the geologically complex region, and economically important deposits are found in arc sequences of all Paleozoic Periods. The economically most productive arcs are Carboniferous. The assessment includes a discussion of the tectonic and geologic setting of porphyry copper deposits in western Central Asia (chapter 1), an application of remote sensing data for hydrothermal alteration mapping as a tool for porphyry copper assessment in the region (chapter 2), and a probabilistic assessment of undiscovered porphyry copper resources in four areas that represent Ordovician and Late Paleozoic (Carboniferous-Permian) magmatic arcs (chapter 3). The principal litho-tectonic terrane concept used to delineate permissive tracts was that of a magmatic arc that formed in the subduction boundary zone above a subducting plate. Eight permissive tracts are delineated on the basis of mapped and inferred subsurface distributions of igneous rocks assigned to magmatic arcs of specified age ranges that define areas where the occurrence of porphyry copper deposits within 1 kilometer of the Earth’s surface is possible. These tracts range in area from about 8,000 to 200,000 square kilometers and host 18 known porphyry copper deposits that contain about 54 million metric tons of copper. Available data included geologic maps, the distribution of significant porphyry copper occurrences and potentially related deposit types, the distribution of hydrothermal alteration patterns that are consistent with porphyry copper mineralization, and information on possible subsurface extensions of permissive rocks. On the basis of analyses of these data, the assessment team estimated a mean of 25 undiscovered porphyry copper deposits for the study area. Estimates of numbers of undiscovered deposits were combined with grade and tonnage models in a Monte Carlo simulation to yield a mean estimate of 95 million metric tons of copper in undiscovered porphyry copper deposits; this represents about twice the amount of identified porphyry copper resources (54 million metric tons). Detailed descriptions of each permissive tract, including the rationales for delineation and assessment, are given in appendixes, along with a geographic information system (GIS) that includes permissive tract boundaries, point locations of known porphyry copper deposits and significant occurrences, and hydrothermal alteration data based on analysis of remote sensing data.

Ural Mountains↗

USGS2011

Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park

The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probabililty, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from 1) field survey site data only, 2) field data combined with data from geospatial models, and 3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 - 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative for areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders; 971-3017 ha for western toads; 4732-16 696 ha for boreal chorus frogs; 4940-19 690 hectares for Columbia spotted frogs.

Yellowstone National Park↗

USGS2017

The Valmy thrust sheet: A regional structure formed during the protracted assembly of the Roberts Mountains allochthon, Nevada, USA

A synthesis of field, biostratigraphic, detrital zircon geochronologic, and remote sensing data across north-central Nevada, United States, defines a thick, regionally extensive sheet of Middle–Upper Ordovician Valmy Formation quartzite that structurally overlies deformed early Paleozoic units of the Roberts Mountains allochthon. Late Paleozoic regional unconformities that record tectonic disruptions have been recognized in the foreland of central and eastern Nevada and locally within the Roberts Mountains allochthon; these identify multiple, regional tectonic events between the Devonian–Mississippian initiation of the Antler orogeny and the Permian–Triassic Sonoma orogeny. However, few studies have documented the regional kinematic history of the Robert Mountains allochthon sensu stricto. In the Independence Mountains of northern Nevada, emplacement of the Roberts Mountains allochthon is restricted to the Mississippian. In the Tuscarora Mountains, the range west and southwest of the Independence Mountains, several deformation events have been identified, and emplacement of the thrust sheet containing the Valmy Formation is restricted to the Late Pennsylvanian–Early Permian. These structural and temporal relations, reflected in the Antler foreland basin adjacent to the Roberts Mountains allochthon and overlap sequences, suggest that the Roberts Mountains allochthon is a composite stratigraphic terrane assembled along the Cordilleran margin during two or more late Paleozoic contractional events. Valmy Formation deposits likely represent the development of coalescing submarine fans below or within bypass channels in a deep slope or rise environment. Petrographic characteristics, biostratigraphy, and detrital zircon U-Pb age populations of the Valmy Formation link it to coeval slope and rise turbidites of the Vinini Formation and shelfal Eureka Quartzite; Valmy Formation detrital zircon age populations are dissimilar to the rift-to-drift facies of the Neoproterozoic–Cambrian Prospect Mountain Quartzite. Throughout north-central Nevada, the Valmy Formation is in fault contact with units of the Roberts Mountains allochthon, including the Devonian–Mississippian Slaven Chert, Silurian–Devonian Elder Sandstone, and Cambrian(?)–Ordovician Vinini Formation, which were deformed prior to, or during, emplacement of the thrust sheet containing Valmy Formation quartzite. Our mapping and data synthesis, guided by regional quartz maps based on remote sensing (Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER]) data, delineate similar structural relationships discontinuously for >200 km along strike of the Roberts Mountains allochthon. Exploration for concealed gold deposits within reach of drilling requires knowledge of the relative thicknesses of the Roberts Mountains allochthon and the Valmy Formation. Overall thicknesses of deformed Roberts Mountains allochthon units between the Valmy Formation and underlying carbonate rocks, which host large, world-class Carlin-type gold deposits, vary by hundreds of meters, but are generally less than 700 m in three of the areas studied here. Recognition of windows through and klippen of the Roberts Mountains allochthon is essential for identification of areas where deposits may be at or near the surface. Correspondingly, most ongoing exploration for Carlin-type gold deposits subjacent to the Roberts Mountains allochthon targets concealed deposits. The model proposed in this study is applicable to determining depth to rocks prospective for undiscovered deposits.

Nevada↗

USGS2011

How landscape dynamics link individual- to population-level movement patterns: A multispecies comparison of ungulate relocation data

Aim To demonstrate how the interrelations of individual movements form large-scale population-level movement patterns and how these patterns are associated with the underlying landscape dynamics by comparing ungulate movements across species. Locations Arctic tundra in Alaska and Canada, temperate forests in Massachusetts, Patagonian Steppes in Argentina, Eastern Steppes in Mongolia. Methods We used relocation data from four ungulate species (barren-ground caribou, Mongolian gazelle, guanaco and moose) to examine individual movements and the interrelation of movements among individuals. We applied and developed a suite of spatial metrics that measure variation in movement among individuals as population dispersion, movement coordination and realized mobility. Taken together, these metrics allowed us to quantify and distinguish among different large-scale population-level movement patterns such as migration, range residency and nomadism. We then related the population-level movement patterns to the underlying landscape vegetation dynamics via long-term remote sensing measurements of the temporal variability, spatial variability and unpredictability of vegetation productivity. Results Moose, which remained in sedentary home ranges, and guanacos, which were partially migratory, exhibited relatively short annual movements associated with landscapes having very little broad-scale variability in vegetation. Caribou and gazelle performed extreme long-distance movements that were associated with broad-scale variability in vegetation productivity during the peak of the growing season. Caribou exhibited regular seasonal migration in which individuals were clustered for most of the year and exhibited coordinated movements. In contrast, gazelle were nomadic, as individuals were independently distributed and moved in an uncoordinated manner that relates to the comparatively unpredictable (yet broad-scale) vegetation dynamics of their landscape. Main conclusions We show how broad-scale landscape unpredictability may lead to nomadism, an understudied type of long-distance movement. In contrast to classical migration where landscapes may vary at broad scales but in a predictable manner, long-distance movements of nomadic individuals are uncoordinated and independent from other such individuals. Landscapes with little broad-scale variability in vegetation productivity feature smaller-scale movements and allow for range residency. Nomadism requires distinct integrative conservation strategies that facilitate long-distance movements across the entire landscape and are not limited to certain migration corridors.

Alaska, Massachusetts↗

USGS1984

Geomorphic domains and linear features on Landsat images, Circle quadrangle, Alaska

A remote sensing study using Landsat images was undertaken as part of the Alaska Mineral Resource Assessment Program (AMRAP). Geomorphic domains A and B, identified on enhanced Landsat images, divide Circle quadrangle south of Tintina fault zone into two regional areas having major differences in surface characteristics. Domain A is a roughly rectangular, northeast-trending area of relatively low relief and simple, widely spaced drainages, except where igneous rocks are exposed. In contrast, domain B, which bounds two sides of domain A, is more intricately dissected showing abrupt changes in slope and relatively high relief. The northwestern part of geomorphic domain A includes a previously mapped tectonostratigraphic terrane. The southeastern boundary of domain A occurs entirely within the adjoining tectonostratigraphic terrane. The sharp geomorphic contrast along the southeastern boundary of domain A and the existence of known faults along this boundary suggest that the southeastern part of domain A may be a subdivision of the adjoining terrane. Detailed field studies would be necessary to determine the characteristics of the subdivision. Domain B appears to be divisible into large areas of different geomorphic terrains by east-northeast-trending curvilinear lines drawn on Landsat images. Segments of two of these lines correlate with parts of boundaries of mapped tectonostratigraphic terranes. On Landsat images prominent north-trending lineaments together with the curvilinear lines form a large-scale regional pattern that is transected by mapped north-northeast-trending high-angle faults. The lineaments indicate possible lithlogic variations and/or structural boundaries. A statistical strike-frequency analysis of the linear features data for Circle quadrangle shows that northeast-trending linear features predominate throughout, and that most northwest-trending linear features are found south of Tintina fault zone. A major trend interval of N.64-72E. in the linear feature data, corresponds to the strike of foliations in metamorphic rocks and magnetic anomalies reflecting compositional variations suggesting that most linear features in the southern part of the quadrangle probably are related to lithologic variations brought about by folding and foliation of metamorphic rocks. A second important trend interval, N.14-35E., may be related to thrusting south of the Tintina fault zone, as high concentrations of linear features within this interval are found in areas of mapped thrusts. Low concentrations of linear features are found in areas of most igneous intrusives. High concentrations of linear features do not correspond to areas of mineralization in any consistent or significant way that would allow concentration patterns to be easily used as an aid in locating areas of mineralization. The results of this remote sensing study indicate that there are several possibly important areas where further detailed studies are warranted.

Alaska↗

USGS2021

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ET a ), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET a , and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ET a yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ET a estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (r ah ) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.

Urucuia Aquifer System↗