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USGS2007

Geologic map of Wupatki National Monument and vicinity, Coconino County, Northern Arizona

Introduction The geologic map of Wupatki National Monument is a cooperative effort between the U.S. Geological Survey, the National Park Service, and the Navajo Nation to provide geologic information for resource management officials of the National Park Service, U.S. Forest Service, Navajo Indian Reservation (herein the Navajo Nation), and visitor information services at Wupatki National Monument, Arizona. Funding for the map was provided in part by the Water Rights Branch of the Water Resources Division of the National Park Service. Field work on the Navajo Nation was conducted under a permit from the Navajo Nation Minerals Department. Any persons wishing to conduct geologic investigations on the Navajo Nation must first apply for, and receive, a permit from the Navajo Nation Minerals Department, P.O. Box 1910, Window Rock, Arizona 86515, telephone (928)-871-6587. Wupatki National Monument lies within the USGS 1:24,000-scale Wupatki NE, Wupatki SE, Wupatki SW, Gray Mountain, East of SP Mountain, and Campbell Francis Wash quadrangles in northern Arizona. The map is bounded approximately by longitudes 111? 16' to 111? 32' 30' W. and latitudes 35? 30' to 35? 37' 40' N. The map area is in Coconino County on the southern part of the Colorado Plateaus geologic province (herein Colorado Plateau). The map area is locally subdivided into three physiographic parts, the Coconino Plateau, the Little Colorado River Valley, and the San Francisco Volcanic Field as defined by Billingsley and others (1997) [fig. 1]. Elevations range from 4,220 ft (1,286 m) at the Little Colorado River near the northeast corner of the map area to about 6,100 ft (1,859 m) at the southwest corner of the map area. The small community of Gray Mountain is about 16 mi (26 km) northwest of Wupatki National Monument Visitor Center, and Flagstaff, Arizona, the nearest metropolitan area, is about 24 mi (38 km) southwest of the Visitor Center (fig. 1). U.S. Highway 89 provides access to the west entrance of Wupatki National Monument. A paved Coconino County road provides a loop from Wupatki National Monument south to Sunset Crater National Monument and back to U.S. Highway 89 about 10 mi (16 km) north of Flagstaff, Arizona. Access to Coconino National Forest is via dirt roads maintained by the National Forest Service. Several unimproved dirt roads on Babbitt Ranch lands provide limited access to remote areas north of Wupatki National Monument. Travel is mostly restricted to paved roads within Wupatki National Monument, and a dirt road that crosses the Little Colorado River provides access to the Navajo Nation area east and northeast of the Little Colorado River. The Little Colorado River crossing is not bridged and can be impassable when the river is flowing. Four-wheel-drive vehicles are recommended but not necessary for travel in remote parts of the Navajo Nation. Extra food and water are highly recommended for travel in this sandy area. Land ownership north of Wupatki National Monument forms a checkerboard pattern between private and State land. Coconino National Forest manages lands south of Wupatki National Monument and the National Park Service manages Wupatki National Monument. The Leupp and Tolani Lake Chapters of the Navajo Nation manage the area northeast and east of the Little Colorado River (see land management boundaries on map). The geologic map of Wupatki National Monument provides updated geologic framework information for this part of the Colorado Plateau. The geologic information supports Federal, State, and private land managers when conducting geologic, biologic, and hydrologic investigations and will support future and ongoing geologic and associated scientific investigations of all disciplines within the Wupatki National Monument area.

Arizona↗

USGS2010

Temperature data from wells in Long Valley Caldera, California

The 30-by-20-km Long Valley Caldera (LVC) in eastern California (fig.1) formed at 0.76 Ma in a cataclysmic eruption that resulted in the deposition of 600 km? of Bishop Tuff outside the caldera rim (Bailey, 1989). By approximately 0.6 Ma, uplift of the central part of the caldera floor and eruption of rhyolitic lava formed the resurgent dome. The most recent eruptive activity in the area occurred approximately 600 yr ago along the Mono-Inyo craters volcanic chain (Bailey, 2004; Hildreth, 2004). LVC hosts an active hydrothermal system that includes hot springs, fumaroles, mineral deposits, and an active geothermal well field and power plant at Casa Diablo along the southwestern boundary of the resurgent dome (Sorey and Lewis, 1976; Sorey and others, 1978; Sorey and others, 1991). Electric power generation began in 1985 with about 10 Mwe net capacity and was expanded to about 40 Mwe (net) in 1991 (Campbell, 2000; Suemnicht and others, 2007). Plans for further expansion are focused mainly on targets in the caldera?s western moat (Sass and Priest, 2002) where the most recent volcanic activity has occurred (Hildreth, 2004). LVC has been the site of extensive research on geothermal resources and volcanic hazards (Bailey and others, 1976; Muffler and Williams, 1976; Miller and others, 1982; Hill and others 2002). The first geothermal exploratory drilling was done in the shallow (< 200 m deep) hydrothermal system at Casa Diablo in the 1960?s (McNitt, 1963). Many more boreholes were drilled throughout the caldera in the 1970?s and 1980?s by private industry for geothermal exploration and by the U.S. Geological Survey (USGS) and Sandia National Laboratory for volcanic and geothermal research and exploration. Temperature logs were obtained in some of these wells during or immediately following drilling, before thermal equilibration was complete. Most of the temperature logs, however, were obtained weeks, months, or years after well completion and are representative of dynamic thermal equilibrium. The maximum reservoir temperature for LVC is estimated to be about 220?C on the basis of chemical geothermometers (Fournier and Truesdell, 1973) using analytical results from water samples collected from a large number of wells and springs across the caldera and around its periphery (Lewis, 1974; Mariner and Wiley, 1976; Farrar and others, 1985, 1987, 1989, White and Peterson, 1991). The deepest well in LVC (~3 km) is the Long Valley Exploratory Well (LVEW) drilled in the 1990?s with funding from the U.S. Department of Energy to investigate the potential for near-magmatic-temperature energy extraction and the occurrence of magma under the central part of the resurgent dome (Finger and Eichelberger, 1990; Finger and Jacobsen, 1999; Sackett and others, 1999). However, temperatures beneath the resurgent dome have proved disappointingly low and in LVEW reach a maximum of only 102 degrees C in a long isothermal section (2,100 to 3,000 m) in Mesozoic basement rocks (Farrar and others, 2003). Temperature data from well logs and geothermometry reveal that the highest temperatures in LVC are beneath the western moat. The hottest temperatures measured in LVC exceed 200 degrees C in two wells (44-16 and RDO-8) located in the western moat. Well 44-16 was drilled through the entire thickness of post-caldera volcanic fill and bottomed in Mesozoic basement. Well RDO-8 was drilled through post-caldera volcanic rocks and 305 m into the Bishop Tuff (Wollenberg and others, 1986). Temperatures in the hydrothermal system decrease toward the east by processes of conduction and dilution from cold groundwater recharge that occurs mostly around the caldera margin and beneath the resurgent dome. Reservoir temperatures at Casa Diablo (fig.1) are about 170?C (for example, MBP-3 and Mammoth-1), decreasing to about 100 degrees C in wells near Hot Creek Gorge (for example, MW-4 and CH-10B), and are generally less than 50?C in thermal springs near Lake

Data Series↗

USGS2023

Critical minerals in subduction-related magmatic-hydrothermal systems of the United States

During the World War and Cold War eras (1910s–1990s), domestic consumption of numerous mineral commodities relied increasingly on imported supplies. Consumption reliance has since expanded to include 50 “critical minerals” (elements and mineral commodities) that are mostly to entirely imported and subject to curtailment by suppliers or supply chain disruption. New domestic supplies of critical minerals are being pursued by mining companies and by several federal departments and agencies. Information on domestic deposits and resources of critical minerals is being compiled by the U.S. Geological Survey Mineral Resources Program, which has organized investigations by mineral system, deposit type, and commodity. Production, reserves, resources, and inventories of 21 critical minerals in domestic magmatic-hydrothermal deposits related to subduction-generated magmatism, and in tailings, slag, slimes, and electrolyte from copper concentrators, smelters, and refineries that processed some deposits, are largely restricted to Western States and Alaska. The critical mineral commodities Al, Sb, As, Bi, Co, fluorite, Ga, Ge, In, Mn, Ni, Nb, Pd, Pt, potash, Re, Ta, Te, Sn, W, and V are variably concentrated in porphyry/skarn copper-(molybdenum), skarn-replacement-vein (S-R-V) tungsten, polymetallic sulfide S-R-V intermediate sulfidation (IS), high-sulfidation gold-silver, low-sulfidation gold-silver, and lithocap alunite deposit types. These deposit types occur in porphyry copper-molybdenum-gold, alkalic porphyry, porphyry tin (granite related), and reduced intrusion-related mineral systems. Production, reserves, and resources of Co, Ni, Nb, Pd, Pt, Ta, Sn, and V in subduction-related deposits in Western States are insignificant to small, mostly equivalent to months to a few years of recent annual domestic consumption (2016–2020). Significant inventories, equivalent to 2 or more years of consumption of aluminum, antimony, potash, and tungsten in unmined S-R-V tungsten, polymetallic sulfide S-R-V-IS, and lithocap alunite deposits vary from approximately 2 to 8 years. Several decades of consumption of arsenic, bismuth, fluorite, gallium, germanium, and indium exist in some polymetallic sulfide S-R-V-IS and lithocap alunite deposit types. Based on concentrations of critical minerals in reserves, resources, drill holes, and deposit domains (ore types), and in captive refinery records, the largest domestic inventories of Sb, As, Bi, Re, and Te, and possibly Ga, Ge, In, Sn, and W, are in porphyry copper-molybdenum (Cu-Mo) deposits in Alaska, Idaho, Utah, and Arizona, and in interim products of processing porphyry Cu-Mo deposit ores for recovery of copper and molybdenum. Concentrations of critical minerals in archival specimens and sample collections, although somewhat biased by collection and conservation decisions and categorization, are broadly proportionate to those in reserves, resources, and drill holes. These concentrations imply significant inventories of some critical minerals in deposits for which production, resources, and refinery records are unavailable or incomplete. Because of the large masses of ores mined and processed annually (hundreds of millions of metric tons) and in reserves and resources (hundreds of millions of metric tons to billions of metric tons), calculated inventories of critical minerals in porphyry Cu-Mo deposits are equivalent to decades and centuries of recent consumption. However, these inventories should not be considered consumable supplies without reserve definition and development of economically viable mining plans and recovery techniques. An expeditious strategy for elimination or reduction of import reliance is recovery, and improved recovery efficiency, of Sb, As, Bi, Re, and Te, and possibly Ga, Ge, In, Ni, Sn, Ti, and W; during concentration and refining of copper and molybdenum minerals in ores of operating porphyry Cu-Mo mines; and in unmined porphyry Cu-Mo resources. These chalcophile, siderophile, and lithophile critical minerals, often undetectable in ore, are concentrated (hundreds of parts per million to percents) in slimes and electrolyte during copper electrorefining or could be recovered, in part, during sulfide concentration and smelting. Other than rhenium (recovered during molybdenum refining) and tellurium, all have been routinely discarded. Subsidization (for example, commodity price guarantees, tax credits, recovery technology development), political initiative, and (or) sustained market favorability could support new production of critical mineral commodities from subduction-related magmatic-hydrothermal deposits in Western States. In addition, insufficient domestic refining capacity could relegate the large inventories of critical minerals in porphyry Cu-Mo reserves and resources (for example, Pebble, Alaska; Resolution and Copper World [Rosemont], Arizona) to exportation in concentrates and importation insecurity, fortifying their present status.

Scientific Investigations Report↗

USGS2013

Baseline groundwater quality from 20 domestic wells in Sullivan County, Pennsylvania, 2012

Water samples were collected from 20 domestic wells during August and September 2012 and analyzed for 47 constituents and properties, including nutrients, major ions, metals and trace elements, radioactivity, and dissolved gases, including methane and radon-222. This study, done in cooperation with the Pennsylvania Department of Conservation and Natural Resources, Bureau of Topographic and Geologic Survey (Pennsylvania Geological Survey), provides a groundwater-quality baseline for central and southern Sullivan County prior to drilling for natural gas in the Marcellus Shale. The analytical results for the 20 groundwater samples collected during this study indicate that only one constituent (gross-alpha radioactivity) in one sample was found to exceed the U.S. Environmental Protection Agency (USEPA) primary drinking water maximum contaminant level (MCL). Water samples from 85 percent of the sampled wells exceeded the proposed USEPA MCL of 300 picocuries per liter (pCi/L) for radon-222; however, only two water samples (10 percent of sampled wells) exceeded the proposed USEPA alternate maximum contaminant level (AMCL) of 4,000 pCi/L for radon-222. In a few samples, the concentrations of total dissolved solids, iron, manganese, and chloride exceeded USEPA secondary maximum contaminant levels (SMCL). In addition, water samples from two wells contained methane concentrations greater than 1 milligram per liter (mg/L). In general, most of the water-quality problems involve aesthetic considerations, such as taste or odor from elevated concentrations of total dissolved solids, iron, manganese, and chloride that develop from natural interactions of water and rock minerals in the subsurface. The total dissolved solids concentration ranged from 31 to 664 mg/L; the median was 130 mg/L. The total dissolved solids concentration in one water sample exceeded the USEPA SMCL of 500 mg/L. Chloride concentrations ranged from 0.59 to 342 mg/L; the median was 12.9 mg/L. The concentration of chloride in one water sample exceeded the USEPA SMCL of 250 mg/L. Concentrations of dissolved iron ranged from less than 3.2 to 6,590 micrograms per liter (µg/L); the median was 11.5 µg/L. The iron concentration in samples from 20 percent of the sampled wells exceeded the USEPA SMCL of 300 µg/L. Concentrations of dissolved manganese ranged from less than 0.13 to 1,710 µg/L; the median was 38.5 µg/L. The manganese concentration in samples from 35 percent of the sampled wells exceeded the USEPA SMCL of 50 µg/L. Activities of radon-222 ranged from 169 to 15,300 picocuries per liter (pCi/L); the median was 990 pCi/L. The gross alpha-particle radioactivity ranged from below detection to 33 pCi/L; the median was 1.5 pCi/L. The gross alpha-particle radioactivity of one water sample exceeded the USEPA MCL of 15 pCi/L. Concentrations of dissolved methane ranged from less than 0.001 to 51.1 mg/L. Methane was not detected in water samples from 13 wells, and the methane concentration was less than 0.07 mg/L in samples from five wells. The highest dissolved methane concentrations were 4.1 and 51.1 mg/L, and the pH of the water from both wells was greater than 8. Water samples from these wells were analyzed for isotopes of carbon and hydrogen in the methane. The isotopic ratio values fell in the range for a thermogenic (natural gas) source. The water samples from these two wells had the highest concentrations of arsenic, boron, bromide, chloride, fluoride, lithium, molybdenum, and sodium of the 20 wells sampled.

Pennsylvania↗

USGS1996

Water quality of surficial aquifers in the Georgia-Florida Coastal Plain

The National Water Quality Assessment Program of the U.S. Geological Survey established the Georgia-Florida Coastal Plain study unit in 1991. The ground-water study-unit survey was conducted in 1993 to provide a broad overview of water quality in surficial aquifers. Three land resource provinces were included in the Georgia-Florida Coastal Plain study-unit survey: the Central Florida Ridge, the Coastal Flatwoods, and the Southern Coastal Plain. The U.S. Geological Survey sampled 37 wells in surficial aquifers, 18 in the Coastal Flatwoods and 19 in the Southern Coastal Plain. The Florida Department of Environmental Protection sampled 27 wells tapping surficial aquifers in the Central Florida Ridge as part of the background ground-water quality monitoring network from 1985 through 1989. The data were used to characterize water quality in surficial aquifers of the Central Florida Ridge. Results of the study-unit survey indicated that dissolved solids concentrations in ground water were mostly less than 100 mg/L (milligrams per liter). Higher medians of pH, specific conductance, and concentrations of calcium, bicarbonate, and dissolved solids were measured in samples from the Central Florida Ridge compared to the Southern Coastal Plain and Coastal Flatwoods, probably because of a greater percentage of carbonate minerals in aquifer materials. The U.S. Environmental Protection Agency secondary maximum contaminant level for iron of 300 ug/L (micrograms per liter) in drinking water was exceeded in 15 of 45 samples. Concentrations of nitrate as nitrogen were less than 3.0 mg/L in most samples (74 percent), indicating little or no influence from human activity. Only five samples (9 percent) had concentrations above 10 mg/L, the U.S. Environmental Protection Agency maximum contaminant level for nitrate concentration in drinking water. Significantly lower median concentrations of nitrate were measured in samples from polyvinyl chloride monitoring wells with diameters less than 6 inches than in large diameter, uncased, or iron-cased wells. The median nitrate concentration was 0.05 mg/L in water from monitoring wells, 1.0 mg/L in samples from iron cased wells, and 2.0 mg/L in samples from uncased wells. Concentrations of volatile organic compounds were mostly less than the detection levels and exceeded 1 ug/L in only four samples. Compounds detected at concentrations greater than 1 ug/L were: tetrachloroethane (8.77 ug/L), toluene (23 ug/L) and chloromethane (21 ug/L). Atrazine, desethyl-atrazine, and metolachlor were the only pesticides detected; concentrations were less than 0.02 ug/L, except for metolachlor (2.5 ug/L). Detection of organic compounds in surficial aquifers may be associated with specific activities or sources near the well. Concentrations of radon exceeded the U.S. Environmental Protection Agency proposed maximum contaminant level of 300 picocuries per liter (pCi/L) in 33 samples from wells on the Coastal Flatwoods and the Southern Coastal Plain. Concentrations as high as 13,000 pCi/L were detected in northern Florida. Although uranium concentrations were less than 1 ug/L in all but one sample (1.3 ug/L) from the Southern Coastal Plain, elevated radon concentrations indicate that uranium is present in aquifer material. Uranium is most likely sorbed to iron oxides and clays in subsurface materials. Tritium concentrations indicated that ground water was recharged by precipitation during the past 40 years. Higher concentrations of tritium in ground water were found in the northern part of the study area and may be related to Savannah River Nuclear Facility.

Florida, Georgia↗

USGS2013

Knowledge and understanding of dissolved solids in the Rio Grande–San Acacia, New Mexico, to Fort Quitman, Texas, and plan for future studies and monitoring

Availability of water in the Rio Grande Basin has long been a primary concern for water-resource managers. The transport and delivery of water in the basin have been engineered by using reservoirs, irrigation canals and drains, and transmountain-water diversions to meet the agricultural, residential, and industrial demand. In contrast, despite the widespread recognition of critical water-quality problems, there have been minimal management efforts to improve water quality in the Rio Grande. Of greatest concern is salinization (concentration of dissolved solids approaching 1,000 mg/L), a water-quality problem that has been recognized and researched for more than 100 years because of the potential to limit both agricultural and municipal use. To address the issue of salinization, water-resource managers need to have a clear conceptual understanding of the sources of salinity and the factors that control storage and transport, identify critical knowledge gaps in this conceptual understanding, and develop a research plan to address these gaps and develop a salinity management program. In 2009, the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers (USACE), New Mexico Interstate Stream Commission (NMISC), and New Mexico Environment Department (NMED) initiated a project to summarize the current state of knowledge regarding the transport of dissolved solids in the Rio Grande between San Acacia, New Mexico, and Fort Quitman, Texas. The primary objective is to provide hydrologic information pertaining to the spatial and temporal variability present in the concentrations and loads of dissolved solids in the Rio Grande, the source-specific budget for the mass of dissolved solids transported along the Rio Grande, and the locations at which dissolved solids enter the Rio Grande. Dissolved-solids concentration data provide a good indicator of the general quality of surface water and provide information on the factors governing salinization within the Rio Grande study area. The pattern in dissolved-solids concentrations along the Rio Grande is one of increasing concentration with increasing distance downstream from Elephant Butte and Caballo Reservoirs. The concentration of dissolved solids in the Rio Grande doubles (approximately 500 to 1,000 mg/L) from below Elephant Butte Reservoir to El Paso and increases by more than a factor of 5 (approximately 500 to 3,200 mg/L) from below Elephant Butte Reservoir to Fort Quitman. Marked increases in the concentration of dissolved solids commonly coincide with contributions from agricultural drains, wastewater-treatment plants, regional groundwater, and upward-flowing saline groundwater. The greatest factor, from the surface-water system, in controlling dissolved solids in the Rio Grande is the amount of water that is being transported or stored. Annual variation in streamflow is influenced primarily by climate (precipitation and evaporation) and management of Elephant Butte and Caballo Reservoirs (water storage and release cycles). Seasonal variation in streamflow within the Rio Grande study area is generally categorized generally as irrigation (March–September) and nonirrigation (October–February) seasons; with streamflow in the Rio Grande is highest during the irrigation season and lowest during the nonirrigation season. Dissolved-solids loads during the irrigation season decrease between Leasburg and Fort Quitman primarily because of irrigation diversions and losses to the underlying alluvial aquifer. Conversely, dissolved-solids loads during the nonirrigation season increase between Caballo Dam and Fort Quitman primarily because of the inflow of dissolved solids from agricultural drains, wastewater-treatment plants, and groundwater with elevated concentrations of dissolved solids. Many studies have mass-balance budgets that account for the mass of dissolved solids transported along the Rio Grande. Results from mass-balance budgets developed for dissolved solids indicated that (1) the inflow of saline groundwater, inflow of regional groundwater, and chemical reactions between mineral phases are the primary sources controlling dissolved solids in the Rio Grande, and (2) groundwater pumping and mineral precipitation are causing a net storage of dissolved solids in the Leasburg to El Paso and El Paso to Fort Quitman reaches of the Rio Grande. Looking forward, multiple water-resource managers from State and local agencies in New Mexico and Texas and Federal agencies formed the Rio Grande Salinity Management Coalition with the goal to reduce the amount of dissolved solids that are transported and stored in the Rio Grande study area. The recommendations for additional monitoring to assist the coalition are as follows: -Monitoring: Couple water-quality and streamflow monitoring in the Rio Grande and agricultural drains; perform groundwater-seepage investigations in the Rio Grande and major agricultural drains; nonitor groundwater water-quality conditions in the Mesilla and Hueco Basins. -Focused Hydrogeology Studies at Inflow Sources: Map dissolved-solids concentrations in the Rio Grande and underlying alluvial aquifer; perform hydrogeologic characterization of subsurface areas containing unusually high concentrations of dissolved solids. -Modeling of Dissolved Solids: Develop models to simulate the transport and storage of dissolved solids in both surface-water and groundwater systems.

New Mexico;Texas↗

USGS2003

Geochemistry of the Birch Creek Drainage Basin, Idaho

The U.S. Survey and Idaho State University, in cooperation with the U.S. Department of Energy, are conducting studies to describe the chemical character of ground water that moves as underflow from drainage basins into the eastern Snake River Plain aquifer (ESRPA) system at and near the Idaho National Engineering and Environmental Laboratory (INEEL) and the effects of these recharge waters on the geochemistry of the ESRPA system. Each of these recharge waters has a hydrochemical character related to geochemical processes, especially water-rock interactions, that occur during migration to the ESRPA. Results of these studies will benefit ongoing and planned geochemical modeling of the ESRPA at the INEEL by providing model input on the hydrochemical character of water from each drainage basin. During 2000, water samples were collected from five wells and one surface-water site in the Birch Creek drainage basin and analyzed for selected inorganic constituents, nutrients, dissolved organic carbon, tritium, measurements of gross alpha and beta radioactivity, and stable isotopes. Four duplicate samples also were collected for quality assurance. Results, which include analyses of samples previously collected from four other sites, in the basin, show that most water from the Birch Creek drainage basin has a calcium-magnesium bicarbonate character. The Birch Creek Valley can be divided roughly into three hydrologic areas. In the northern part, ground water is forced to the surface by a basalt barrier and the sampling sites were either surface water or shallow wells. Water chemistry in this area was characterized by simple evaporation models, simple calcite-carbon dioxide models, or complex models involving carbonate and silicate minerals. The central part of the valley is filled by sedimentary material and the sampling sites were wells that are deeper than those in the northern part. Water chemistry in this area was characterized by simple calcite-dolomite-carbon dioxide models. In the southern part, ground water enters the ESRPA. In this area, the sampling sites were wells with depths and water levels much deeper than those in the northern and central parts of the valley. The calcium and carbon water chemistry in this area was characterized by a simple calcite-carbon dioxide model, but complex calcite-silicate models more accurately accounted for mass transfer in these areas. Throughout the geochemical system, calcite precipitated if it was an active phase in the models. Carbon dioxide either precipitated (outgassed) or dissolved depending on the partial pressure of carbon dioxide in water from the modeled sites. Dolomite was an active phase only in models from the central part of the system. Generally the entire geochemical system could be modeled with either evaporative models, carbonate models, or carbonate-silicate models. In both of the latter types of models, a significant amount of calcite precipitated relative to the mass transfer to and from the other active phases. The amount of calcite precipitated in the more complex models was consistent with the amount of calcite precipitated in the simpler models. This consistency suggests that, although the simpler models can predict calcium and carbon concentrations in Birch Creek Valley ground and surface water, silicate-mineral-based models are required to account for the other constituents. The amount of mass transfer to and from the silicate mineral phases was generally small compared with that in the carbonate phases. It appears that the water chemistry of well USGS 126B represents the chemistry of water recharging the ESRPA by means of underflow from the Birch Creek Valley.

Idaho↗

USGS1998

Hydrology and geochemistry of a slag-affected aquifer and chemical characteristics of slag-affected ground water, northwestern Indiana and northeastern Illinois

Slag is a by-product of steel manufacturing and a ubiquitous fill material in northwestern Indiana. Ground water associated with slag deposits generally is characterized by high pH and elevated concentrations of many inorganic water-quality constituents. The U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, conducted a study in northwestern Indiana from June 1995 to September 1996 to improve understanding of the effects of slag deposits on the water quality of a glacial-outwash aquifer. The Bairstow Landfill, a slag-fill deposit overlying the Calumet aquifer near Hammond, Indiana, was studied to represent conditions in slag-deposit settings that are common in northwestern Indiana. Ground water from 10 observation wells, located in four nests at the site, and surface water from the adjacent Lake George were analyzed for values of field-measured parameters and concentrations of major ions, nutrients, trace elements, and bulk properties. Solid-phase samples of slag and aquifer sediment collected during drilling were examined with X-ray diffraction and geochemical digestion and analysis. Concentrations of calcium, potassium, sodium, and sulfate were highest in wells screened partly or fully in slag. Potassium concentrations in ground water ranged from 2.9 to 120 milligrams per liter (mg/L), were highest in water from slag deposits, and decreased with depth. The highest concentrations for aluminum, barium, molybdenum, nickel, and selenium were in water from the slag. Silica concentrations were highest in wells screened directly beneath the slag-aquifer interface, and magnesium concentrations were highest in intermediate and deep aquifer wells. Silica concentrations in shallow and intermediate aquifer wells ranged from 27 to 41 mg/L and were about 10 times greater than those in water from slag deposits. The highest concentrations for chromium, lead, and zinc were in ground water from immediately below the slag-aquifer interface. The solid-phase analyses indicated that calcite, dolomite, and quartz generally were present throughout the slag-aquifer system; barian celestite, cristobalite, manganese-bearing calcite, and minrecordite were present in fewer samples. Trace elements that are liberated from the slag may be incorporated as impurities during precipitation of major minerals, sorbed onto clays and other grainsize fractions not analyzed as part of this study, or present in low-abundance minerals that were not detected by the X-ray analysis. Mass-balance and speciation programs were used to identify geochemical processes that may be occurring as water infiltrates through the slag, flows into the aquifer, and discharges into Lake George. The geochemical models indicate that precipitation of calcite may be occurring where slag-affected water enters the aquifer. Models also indicate that dolomite precipitation and clay-mineral dissolution may be occurring at the slag-aquifer interface; however, dolomite precipitation is generally believed to require geologically long time periods. Silica may be dissolving where slag-affected ground water enters the aquifer and may be precipitating where slag-affected ground water discharges to the lakebed of Lake George. In addition to the site-specific study, a statistical analysis of regional water quality was done to compare ground water in wells affected and unaffected by slag. When com-pared to wells in background locations in the Calumet aquifer, wells screened in slag across northwestern Indiana and northeastern Illinois generally had relatively higher pH and specific-conductance values and relatively higher concentrations of alkalinity, dissolved solids, suspended solids, total organic carbon, calcium, potassium, sodium, chloride, aluminum, barium, and possibly magnesium, sulfate, chromium, cobalt, copper, cyanide, manganese, mercury, nickel, and vanadium. When compared to wells in slag and wells in background locations, ground water from immediately beneath or immediately downgradient from slag had relatively high concentrations of arsenic and silica. Water-quality characteristics in ground water at the Bairstow Landfill were similar to water-quality characteristics in slag-contact and slag-affected wells throughout northwestern Indiana.

Illinois, Indiana↗

USGS2018

Baseline water quality of an area undergoing shale-gas development in the Muskingum River watershed, Ohio, 2015–16

In 2015–16, the U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District, led a study to assess baseline (2015–16) surface-water quality in six lake drainage basins within the Muskingum River watershed that are in the early years of shale-gas development. In 2015, 9 of the 10 most active counties in Ohio for oil and gas development were wholly or partially within the Muskingum River watershed. In addition to shale gas development, the area has a history of conventional oil and gas development and coal mining. In all, 30 surface-water sites were sampled: 20 in tributaries flowing to the lakes, 4 in lakes themselves, and 6 downstream of the lakes. At each of the 30 sites, 6 samples were collected to characterize surface-water chemistry throughout a range of hydrologic conditions. The sampling generally occurred during low flows (periods of greater groundwater contribution) rather than during runoff events (periods of high stream stage). Trilinear diagrams of major ion chemistry revealed three main types of water in the study area―sulfate-dominated waters, bicarbonate-dominated waters, and waters with mixed bicarbonate and chloride anions. Most sites produced samples of bicarbonate-dominated water, and 11 sites produced samples with sulfate-type waters. Mixed bicarbonate and chloride waters were found in samples from two of the six lake drainage basins studied. The baseline (2015–16) assessment of surface-water quality in the study area indicated that few water-chemistry constituents and properties occurred at concentrations or levels that would adversely affect aquatic organisms. Chemical-specific, aquatic life use criteria were not met in only three instances: two were for total dissolved solids at sites likely impacted by coal mining in their drainage basins (hereafter referred to as “mine-impacted sites”), and one was for dissolved oxygen. Mine drainage from historical coal mining in the region likely affected the quality of about one-third of the streams sampled. To simplify interpretation of water-chemistry results, 11 sites with sulfate-type water were identified as mine-impacted sites based on water-quality criteria established by Ohio Department of Natural Resources, Division of Mineral Resources Management, and separated out for subsequent statistical analysis. Concentrations or levels of bicarbonate, boron, calcium, carbonate, total dissolved solids, fluoride, magnesium, lithium, pH, potassium, sodium, specific conductance, strontium, sulfate, and suspended sediment in water were higher (significance level of 0.05) at mine-impacted stream sites than at non-mine-impacted stream sites. An accidental release of oil- and gas-related brines could increase salinity (sodium and chloride), the concentration of total dissolved solids in shallow groundwater and streams, and specific conductance. For this study, chloride concentrations in the study area ranged from 2.12 to 76.1 milligrams per liter. Sources of chloride in water samples were evaluated using binary mixing curves and ratios of chloride to bromide. These ratios indicated that 13 samples from 3 sites in the drainage basin that contained the highest density of conventional oil and gas wells in the study, as well as 4 samples collected from other drainage basins, likely contained a component of brine. Concentrations or levels of barium, bromide, chloride, iron, lithium, manganese, and sodium were significantly higher (alpha = 0.05) in samples with a component of brine than in samples without a component of brine. Benzene, toluene, ethylbenzene and xylene (BTEX), compounds that occur naturally in crude oil, made up 24 of the 45 detections (53 percent) of volatile organic compounds in the study area. The BTEX detections were not associated with sites containing a component of brine. The only volatile organic compound detected in any of the 17 samples that contained a component of brine was acetone, detected in 3 (18 percent) of these samples and in 11 percent of samples not containing a component of brine. Considering that BTEX are gasoline hydrocarbons and that most of the detections occurred during warmer months in and around the lakes, the BTEX detections likely are associated with increases in outdoor activities such as automobile and boating traffic. Radium-226 and radium-228 were included in the list of analytes for this study because production water from shale-gas drilling can contain these naturally occurring radioactive materials. Concentrations of radium-226 exceeded background levels in only two surface-water samples. Concentrations of radium-228 exceeded background levels in one surface-water sample. A brine signature potentially indicative of oil and gas contamination was detected in samples collected at two sites that contained active or plugged waste injection wells, or both. Results from the study indicated significant differences in the median concentrations of bromide, chloride, lithium, manganese, sodium, and total dissolved nitrogen between sites with and without injection wells in their drainage areas. Median concentrations of bromide, chloride, lithium, and sodium, which are common oil- and gas-related contaminants, were higher at sites with injection wells in their drainage areas compared to sites without injection wells. Historical (1960s, 1970s, and 1980s) chloride concentrations and streamflow data at or near five of the six sampling sites downstream from each lake dam were compared to current (2015–16) values. An analysis of covariance was done to test the effects of streamflow, time (decade), and the combined effects (cross product) of streamflow and time on chloride concentrations. Those analyses indicated that streamflow was not significant in explaining the variation in chloride concentration, likely because streamflow in those locations is controlled by dam operations; therefore, association between runoff-generating events and streamflow is less direct than in unregulated streams. From the 1980s to the study period (2015–16), data for three of the five lakes indicated an increase in chloride concentrations. The comparison of historical and current (2015–16) study data from samples collected at another lake indicated that chloride concentrations increased from the 1960s to the 1970s, but concentrations in the 1970s and 2015–16 were similar even though 13 samples from this lake drainage basin were classified as having a component of brine. Median chloride concentrations for the fifth lake, however, seemed to decrease from the 1980s to 2015–16.

Ohio↗

USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the South Bamyan mineral district in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the South Bamyan mineral district, which has areas with a spectral reflectance anomaly that require field investigation. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2006,2007, 2008),but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for South Bamyan) and the WGS84 datum. The final image mosaics for the South Bamyan area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.

South Bamyan Mineral District↗

USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Katawas mineral district in Afghanistan: Chapter N in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Katawas mineral district, which has gold deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©AXA, 2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Katawas) and the WGS84 datum. The final image mosaics are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Katawas study area, one subarea was designated for detailed field investigation (that is, the Gold subarea); this subarea was extracted from the area's image mosaic and is provided as a separate embedded geotiff image.

Katawas Mineral District↗

USGS2013

U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration

Executive Summary Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being. The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking: Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. Develop tools, technologies, and capacities to inform decisionmaking about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. Support decisionmaking. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Circular↗

USGS2012

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Balkhab mineral district in Afghanistan: Chapter B in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Balkhab mineral district, which has copper deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2007,2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Balkhab) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Balkhab area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Balkhab study area, one subarea was designated for detailed field investigations (that is, the Balkhab Prospect subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.

Balkh;Samangan;Sari-Pul↗

USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Kandahar mineral district, which has bauxite deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA,2006,2007,2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar- elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image- registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative- reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area- enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Kandahar) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Kandahar area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Kandahar study area, two subareas were designated for detailed field investigations (that is, the Obatu-Shela and Sekhab-Zamto Kalay subareas); these subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.

Kandahar Mineral District↗

USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Nalbandon mineral district in Afghanistan: Chapter L in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Nalbandon mineral district, which has lead and zinc deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2007, 2008, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (41 for Nalbandon) and the WGS84 datum. The final image mosaics were subdivided into ten overlapping tiles or quadrants because of the large size of the target area. The ten image tiles (or quadrants) for the Nalbandon area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Nalbandon study area, two subareas were designated for detailed field investigations (that is, the Nalbandon District and Gharghananaw-Gawmazar subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Nalbandon Mineral District↗

USGS2014

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Farah mineral district in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Farah mineral district, which has spectral reflectance anomalies indicative of copper, zinc, lead, silver, and gold deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 2007, 2008, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Farah) and the WGS84 datum. The final image mosaics were subdivided into four overlapping tiles or quadrants because of the large size of the target area. The four image tiles (or quadrants) for the Farah area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Farah study area, five subareas were designated for detailed field investigations (that is, the FarahA through FarahE subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Farah Mineral District↗

USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Panjsher Valley mineral district in Afghanistan: Chapter M in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Panjsher Valley mineral district, which has emerald and silver-iron deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2009, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Panjsher Valley) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Panjsher Valley area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Panjsher Valley study area, two subareas were designated for detailed field investigations (that is, the Emerald and Silver-Iron subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Panjsher Valley Mineral District↗

USGS2012

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Zarkashan mineral district, which has copper and gold deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2006,2007, 2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Zarkashan) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Zarkashan area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Zarkashan study area, three subareas were designated for detailed field investigations (that is, the Mine Area, Bolo Gold Prospect, and Luman-Tamaki Gold Prospect subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Zarkashan Mineral District↗