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At least 1,207 records · Page 67Linked to original sources
USGS2002

Fifty-year flood-inundation maps for Juticalpa, Honduras

After the devastating floods caused by Hurricane Mitch in 1998, maps of the areas and depths of 50-year-flood inundation at 15 municipalities in Honduras were prepared as a tool for agencies involved in reconstruction and planning. This report, which is one in a series of 15, presents maps of areas in the municipality of Juticalpa that would be inundated by a 50-year flood of Rio Juticalpa. Geographic Information System (GIS) coverages of the flood inundation are available on a computer in the municipality of Juticalpa as part of the Municipal GIS project and on the Internet at the Flood Hazard Mapping Web page (http://mitchnts1.cr.usgs.gov/projects/floodhazard.html). These coverages allow users to view the flood inundation in much more detail than is possible using the maps in this report. Water-surface elevations for a 50-year-flood on Rio Juticalpa at Juticalpa were estimated using HEC-RAS, a one-dimensional, steady-flow, step-backwater computer program. The channel and floodplain cross sections used in HEC-RAS were developed from an airborne light-detection-and-ranging (LIDAR) topographic survey of the area. The estimated 50-year-flood discharge for Rio Juticalpa at Juticalpa, 1,360 cubic meters per second, was computed as the drainage-area-adjusted weighted average of two independently estimated 50-year-flood discharges for the gaging station Rio Juticalpa en El Torito, located about 2 kilometers upstream from Juticalpa. One discharge, 1,551 cubic meters per second, was estimated from a frequency analysis of the 33 years of peak-discharge record for the gage, and the other, 486 cubic meters per second, was estimated from a regression equation that relates the 50-year-flood discharge to drainage area and mean annual precipitation. The weighted-average of the two discharges at the gage is 1,310 cubic meters per second. The 50-year flood discharge for the study area reach of Rio Juticalpa was estimated by multiplying the weighted discharge at the gage by the ratio of the drainage areas upstream from the two locations.

Juticalpa↗
USGS2001

Pollen-limited reproduction in blue oak: Implications for wind pollination in fragmented populations

Human activities are fragmenting forests and woodlands worldwide, but the impact of reduced tree population densities on pollen transfer in wind-pollinated trees is poorly understood. In a 4-year study, we evaluated relationships among stand density, pollen availability, and seed production in a thinned and fragmented population of blue oak (Quercus douglasii). Geographic coordinates were established and flowering interval determined for 100 contiguous trees. The number of neighboring trees within 60 m that released pollen during each tree's flowering period was calculated and relationships with acorn production explored using multiple regression. We evaluated the effects of female flower production, average temperature, and relative humidity during the pollination period, and number of pollen-producing neighbors on individual trees' acorn production. All factors except temperature were significant in at least one of the years of our study, but the combination of factors influencing acorn production varied among years. In 1996, a year of large acorn crop size, acorn production was significantly positively associated with number of neighboring pollen producers and density of female flowers. In 1997, 1998, and 1999, many trees produced few or no acorns, and significant associations between number of pollen-producing neighbors and acorn production were only apparent among moderately to highly reproductive trees. Acorn production by these reproductive trees in 1997 was significantly positively associated with number of neighboring pollen producers and significantly negatively associated with average relative humidity during the pollination period. In 1998, no analysis was possible, because too few trees produced a moderate to large acorn crop. Only density of female flowers was significantly associated with acorn production of moderately to highly reproductive trees in 1999. The effect of spatial scale was also investigated by conducting analyses with pollen producers counted in radii ranging from 30 m to 80 m. The association between number of pollen-producing neighbors and acorn production was strongest when neighborhood sizes of 60 m or larger were considered. Our results suggest that fragmentation and thinning of blue oak woodlands may reduce pollen availability and limit reproduction in this wind-pollinated species.

Oecologia↗
USGS2009

Climate in the dry central Andes over geologic, millenial, and interannual timescales

Over the last eight years, we have developed several paleoenvironmental records from a broad geographic region spanning the Altiplano in Bolivia (18°S–22°S) and continuing south along the western Andean flank to ca. 26°S. These records include: cosmogenic nuclide concentrations in surface deposits, dated nitrate paleosoils, lake levels, groundwater levels from wetland deposits, and plant macrofossils from urine-encrusted rodent middens. Arid environments are often uniquely sensitive to climate perturbations, and there is evidence of significant changes in precipitation on the western flank of the central Andes and the adjacent Altiplano. In contrast, the Atacama Desert of northern Chile is hyperarid over many millions of years. This uniquely prolonged arid climate requires the isolation of the Atacama from the Amazon Basin, a situation that has existed for more than 10 million years and that resulted from the uplift of the Andes and/or formation of the Altiplano plateau. New evidence from multiple terrestrial cosmogenic nuclides, however, suggests that overall aridity is occasionally punctuated by rare rainfall events that likely originate from the Pacific. East of the hyperarid zone, climate history from multiple proxies reveals alternating wet and dry intervals where changes in precipitation originating from the Atlantic may exceed 50%. An analysis of Pleistocene climate records across the region allows reconstruction of the spatial and temporal components of climate change. These Pleistocene wet events span the modern transition between two modes of interannual precipitation variability, and regional climate history for the Central Andean Pluvial Event (CAPE; ca. 18–8 ka) points toward similar drivers of modern interannual and past millennial-scale climate variability. The north-northeast mode of climate variability is linked to El Niño–Southern Oscillation (ENSO) variability, and the southeast mode is linked to aridity in the Chaco region of Argentina.

Andes↗
USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗
USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗
USGS2026

Decadal trends in the quality of groundwater used for public drinking-water supply in California, 2004–23, California Groundwater Ambient Monitoring and Assessment Program, Priority Basin Project

This study provides a comprehensive assessment of decadal changes in the quality of groundwater used for public drinking-water supply at 444 monitoring sites across California during 2004–23. We assessed decadal step trends in groundwater quality for 145 water-quality constituents and geochemical indicators statewide and across geographic and land-use based network groups. We evaluated the statistical significance of directional changes (predominant increase or decrease of constituent concentrations) and the magnitude of those changes across all network groups. Uranium showed the most widespread directional and high-magnitude increases of all constituents with regulatory benchmarks statewide, particularly in the agriculture-dominated Central Valley as well as urban-and desert-dominated regions of Southern California. Fluoride and perchlorate showed the most widespread directional and high-magnitude decreases of all constituents with regulatory benchmarks statewide, which were also most pronounced in Southern California. Although arsenic and nitrate did not often register significant directional changes across network groups, they showed widespread, high-magnitude changes in both directions (increase and decrease) at levels often exceeding 10 percent of respective regulatory benchmarks statewide. Triazine herbicides (atrazine and simazine) and the gasoline oxygenate methyl tert -butyl ether showed significant directional decreases statewide, but not at levels considered to be of high magnitude compared to respective regulatory benchmarks. We observed significant directional and high-magnitude increases of total dissolved solids statewide, which were most pronounced in agricultural areas. Analysis of explanatory geochemical indicators indicated that prevalent statewide increases of alkalinity and calcium were the predominant components of the observed statewide increases in total dissolved solids by mass. Widespread increases in groundwater alkalinity and calcium across agricultural and urban areas may be related, in part, to warm-season irrigation and other anthropogenic factors that have shifted soil weathering dynamics over the long term. Increasing alkalinity concentrations were related to increasing uranium concentrations, particularly in areas with aquifer materials derived from granitic rocks. Conversely, increasing calcium concentrations were related to decreasing fluoride concentrations, particularly in areas where fluoride occurred naturally at elevated concentrations. Decrease of perchlorate, triazine herbicides, and methyl tert -butyl ether are likely related to decreased anthropogenic source inputs over time and natural attenuation in aquifers.

California↗
USGS2009

Natural offshore oil seepage and related tarball accumulation on the California coastline — Santa Barbara Channel and the southern Santa Maria Basin; source identification and inventory

Oil spillage from natural sources is very common in the waters of southern California. Active oil extraction and shipping is occurring concurrently within the region and it is of great interest to resource managers to be able to distinguish between natural seepage and anthropogenic oil spillage. The major goal of this study was to establish the geologic setting, sources, and ultimate dispersal of natural oil seeps in the offshore southern Santa Maria Basin and Santa Barbara Basins. Our surveys focused on likely areas of hydrocarbon seepage that are known to occur between Point Arguello and Ventura, California. Our approach was to 1) document the locations and geochemically fingerprint natural seep oils or tar; 2) geochemically fingerprint coastal tar residues and potential tar sources in this region, both onshore and offshore; 3) establish chemical correlations between offshore active seeps and coastal residues thus linking seep sources to oil residues; 4) measure the rate of natural seepage of individual seeps and attempt to assess regional natural oil and gas seepage rates; and 5) interpret the petroleum system history for the natural seeps. To document the location of sub-sea oil seeps, we first looked into previous studies within and near our survey area. We measured the concentration of methane gas in the water column in areas of reported seepage and found numerous gas plumes and measured high concentrations of methane in the water column. The result of this work showed that the seeps were widely distributed between Point Conception east to the vicinity of Coal Oil Point, and that they by in large occur within the 3-mile limit of California State waters. Subsequent cruises used sidescan and high resolution seismic to map the seafloor, from just south of Point Arguello, east to near Gaviota, California. The results of the methane survey guided the exploration of the area west of Point Conception east to Gaviota using a combination of seismic instruments. The seafloor was mapped by sidescan sonar, and numerous lines of high -resolution seismic surveys were conducted over areas of interest. Biomarker and stable carbon isotope ratios were used to infer the age, lithology, organic matter input, and depositional environment of the source rocks for 388 samples of produced crude oil, seep oil, and tarballs mainly from coastal California. These samples were used to construct a chemometric fingerprint (multivariate statistics) decision tree to classify 288 additional samples, including tarballs of unknown origin collected from Monterey and San Mateo County beaches after a storm in early 2007. A subset of 9 of 23 active offshore platform oils and one inactive platform oil representing a few oil reservoirs from the western Santa Barbara Channel were used in this analysis, and thus this model is not comprehensive and the findings are not conclusive. The platform oils included in this study are from west to east: Irene, Hildago, Harvest, Hermosa, Heritage, Harmony, Hondo, Holly, Platform A, and Hilda (now removed). The results identify three “tribes” of 13 C-rich oil samples inferred to originate from thermally mature equivalents of the clayey-siliceous, carbonaceous marl, and lower calcareous-siliceous members of the Monterey Formation. Tribe 1 contains four oil families having geochemical traits of clay-rich marine shale source rock deposited under suboxic conditions with substantial higher-plant input. Tribe 2 contains four oil families with intermediate traits, except for abundant 28,30-bisnorhopane, indicating suboxic to anoxic marine marl source rock with hemipelagic input. Tribe 3 contains five oil families with traits of distal marine carbonate source rock deposited under anoxic conditions with pelagic but little or no higher-plant input. Tribes 1 and 2 occur mainly south of Point Conception in paleogeographic settings where deep burial of the Monterey Formation source rock favored generation from all three members or their equivalents. In this area, oil from the clayey-siliceous and carbonaceous marl members (Tribes 1 and 2) may overwhelm that from the lower calcareous-siliceous member (Tribe 3) because the latter is thinner and less oil-prone than the overlying members. Tribe 3 occurs mainly north of Point Conception, where shallow burial caused preferential generation from the underlying lower calcareous-siliceous member or another unit with similar characteristics. It is very desirable to be able to clearly distinguish the naturally occurring seep oils from the anthropogenically derived platform oils. Within the “training set” of oils and tars (388 samples), the biomarker parameters are sometimes sufficient to allow unique discrimination of individual platform oils. More often however, platform samples and seep samples with sources geographically close to each other are too similar to each other, with respect to the biomarker parameters, to definitively differentiate them on that basis alone. In some cases other parameters can be helpful. These other parameters are related to the degree of biogeochemical degradation or weathering that the oils or tars have experienced. These components include the typical oil distribution of n-alkane hydrocarbons and isoprenoids pristane and phytane. All of the platform oils in our sample set contain these components. On the other hand, the seep oils or tars have been exposed to significant biodegradation while in the near subsurface. The majority, but not all of seep oils or tars have been biodegraded up to or beyond the loss of n-alkanes and isoprenoids. Seep oils found in the vicinity of Coal Oil Point or Arroyo Burro are apparently the least weathered and are particularly likely to retain significant n-alkanes and isoprenoids. Therefore the combination of chemometric fingerprinting and the presence or absence of n-alkanes and isoprenoids help to differentiate anthropogenic production oils versus natural seeps oils and tars. The differentiation is not always definitive because of the close chemical similarity of some samples and the variability in the biodegradation progression. This is the case near Coal Oil Point, and near Platform A (Dos Cuadros Field) where seep oils and Platform Holly and Platform A oils are genetically very similar and cannot be definitively distinguished after a period of a few days of weathering. In contrast, oils from the Point Conception platforms can be distinguished on the basis of chemometric fingerprinting alone. In the middle of this spectrum are oils from Platforms Harmony, Heritage, and Hondo, where it is expected that oil weathering would take on the order of two weeks to a month to produce tarballs similar to those seen near Point Conception. In this case there is a much greater degree of weathering needed to proceed from produced oil to the biodegraded tar characteristic of tarball stranded on the beach. Tar deposition on beaches was monitored as part of cooperative with the County of Santa Barbara Energy Division and the U.S. Geological Survey during 2001-2003. We found tar deposition varies on a seasonal basis. In general, tarballs accumulate at a faster rate or remain longer on all beaches during the summer and fall months. The reasons for this are unclear based on our limited observations, however we speculate that factors such as prevailing winds and currents combined with more quiescent wave conditions favors the accumulation and preservation of tarballs on the beach during the summer and fall months. In contrast, winter storms, with much greater wave action remove beach sand and other materials, and stormy seas tend to break up oil that might weather into tarballs. Natural seepage is affected by the spring/neap tidal cycle; however, the link to tar deposition is unclear. Longer periods of monitoring are needed to address the variability in the data and provide a more robust statistical analysis.

California↗
USGS2026

Decadal trends in the quality of groundwater used for public drinking-water supply in California, 2004–2023, California groundwater ambient monitoring and assessment program, priority basin project

This study provides a comprehensive assessment of decadal changes in the quality of groundwater used for public drinking-water supply at 444 monitoring sites across California during 2004–2023. We assessed decadal step trends in groundwater quality for 145 water-quality constituents and geochemical indicators statewide and across geographic and land-use based network groups. We evaluated the statistical significance of directional changes (predominant increase or decrease of constituent concentrations) and the magnitude of those changes across all network groups. Uranium showed the most widespread directional and high-magnitude increases of all constituents with regulatory benchmarks statewide, particularly in the agriculture-dominated Central Valley as well as urban- and desert-dominated regions of Southern California. Fluoride and perchlorate showed the most widespread directional and high-magnitude decreases of all constituents with regulatory benchmarks statewide, which were also most pronounced in Southern California. Although arsenic and nitrate did not often register significant directional changes across network groups, they showed widespread, high-magnitude changes in both directions (increase and decrease) at levels often exceeding 10 percent of respective regulatory benchmarks statewide. Triazine herbicides (atrazine and simazine) and the gasoline oxygenate methyl tert-butyl ether (MTBE) showed significant directional decreases statewide, but not at levels considered to be of high magnitude compared to respective regulatory benchmarks. We observed significant directional and high-magnitude increases of total dissolved solids (TDS) statewide, which were most pronounced in agricultural areas. Analysis of explanatory geochemical indicators indicated that prevalent statewide increases of alkalinity and calcium were the predominant components of the observed statewide increases in TDS by mass. Widespread increases in groundwater alkalinity and calcium across agricultural and urban areas may be related, in part, to warm-season irrigation and other anthropogenic factors that have shifted soil weathering dynamics over the long term. Increasing alkalinity concentrations were related to increasing uranium concentrations, particularly in areas with aquifer materials derived from granitic rocks. Conversely, increasing calcium concentrations were related to decreasing fluoride concentrations, particularly in areas where fluoride occurred naturally at elevated concentrations. Decrease of perchlorate, triazine herbicides, and MTBE are likely related to decreased anthropogenic source inputs over time and natural attenuation in aquifers.

EarthArXiv↗
USGS1960

A statistical analysis of the distribution of a larval nematode ( Anisakis sp. ) in the musculature of chum salmon ( Oncorhynchus keta - Walbaum)

The pepsin-HCl digestion technique is probably the best method of isolating Anisakis larvae from the musculature of chum salmon. Some losses can be expected due to breakage of the resistant cuticle of Anisakis , and can be estimated to be about 6% when counting the parasites with the unaided eye. Comparisons of Anisakis infections of the dorsal and ventral musculature indicated that very few larvae are found in the dorsal portions (above the lateral line). Forty-seven per cent of the fish had no dorsal infections, and 90% of the fish had less than 10% of total infection in the dorsal musculature. Comparisons of Anisakis infection ratios of the right and left sides below the lateral line showed some variation. Chi-square tests of the hypothesis that 50% of the infections occur in either side indicated no significant differences. There were indications that large total infection intensities displayed less variation. Comparisons of Anisakis infections of the anterior and posterior portions of the lateral musculature indicated that the infections were greater in the anterior portions. Calculated 95% confidence limits showed 50.0 to 59.0% infection in the anterior portion compared to 41.0 to 50.0% in the posterior portion. Analyses of the Anisakis distributions within the anterior portions of the lateral musculature were hindered by extreme variation in the counts. In general, an average of 60% of the anterior portion infections occurred in the upper two quadrants of musculature, and 40% in the lower two quadrants. Changes in distributional patterns were affected by total parasite density, rather than by age or geographical location. For purposes of comparing infection rates in chum salmon, distributional patterns indicate that the minimum sample of musculature which should be taken from each fish is one side below the lateral line. The one factor which probably contributes the greatest effect on distributional patterns of Anisakis within chum salmon musculature is the total intensity of infection (or population density of Anisakis ) in each fish.

Experimental Parasitology↗
USGS2003

Development of regression equations to estimate flow durations and low-flow-frequency statistics in New Hampshire streams

Regression equations and basin-characteristic digital datasets were developed to help water-resource managers estimate surface-water resources during periods of low flow in New Hampshire. The regression equations were developed to estimate statistics for the seasonal and annual low-flow-frequency and seasonal period-of-record and period-of-record flow durations. Because streamflow is maintained by ground-water discharge during periods of low flow, these equations also will aid in the assessment of ground-water availability. Ultimately, the equations and datasets developed herein can be combined with data on water withdrawals, discharges, and interbasin transfers in a geographic information system (GIS) to allow assessments of water use and water availability in any drainage basin in the State of New Hampshire. Regression equations developed in this study provide estimates of the seasonal (spring, summer, fall, and winter) and annual 7-day 2-year (7Q2) and 7-day 10-year (7Q10) low-flow-frequency values, as well as seasonal period-of-record and period-of-record flow durations (60-, 70-, 80-, 90-, 95-, and 98-percent exceedences) for ungaged reaches of unregulated New Hampshire streams. Regression equations were developed using seasonal and annual low-flow statistics from 58 to 60 continuous-record stream-gaging stations in New Hampshire and nearby areas in neighboring states, and measurements of various characteristics of the drainage basins that contribute flow to those stations. The estimating equations for the seasonal and annual 7Q2 and 7Q10 values were developed using generalized-least-squares (GLS) regression analyses. The GLS equations developed for these flow statistics gave average prediction errors that ranged from 11 to 61 percent. The estimating equations for flow-duration exceedence frequency values were developed using ordinary-least-squares (OLS) regression analysis. The OLS equations developed for these flow statistics gave average prediction errors ranging from 14 to 79 percent. A total of 93 measurable drainage-basin characteristics were selected as possible predictor variables. Of these 93 variables, the following 10 were determined to be statistically significant predictors for at least one of the dependent variables: drainage area, average basin slope, maximum basin elevation, average summer gage precipitation for 1961-90, average spring gage precipitation for 1961-90, average mean annual basin temperature for 1961-90, average mean summer basin temperature for 1961-90, average winter basin-centroid precipitation for 1961-90, percent of the basin that is coniferous, and percent of the basin that is mixed coniferous and deciduous. These 10 basin characteristics were selected because they were statistically significant based on several statistical parameters that evaluated which combination of characteristics contributed the most to the predictive accuracy of the regression-equation models. A GIS is required to measure the values of the predictor variables for the equations developed in this study.

New Hampshire↗
USGS2006

Textural analysis of marine sediments at the USGS Woods Hole Science Center; methodology and data on DVD

Marine sediments off the eastern United States vary markedly in texture (i.e., the size, shape, composition, and arrangement of their grains) due to a complex geologic history. For descriptive purposes, however, it is typically most useful to classify these sediments according to their grain-size distributions. In 1962, the U.S. Geological Survey began a program to study the marine geology of the continental margin off the Atlantic coast of the United States. As part of this program and numerous subsequent projects, thousands of sediment grab samples and cores were collected and analyzed for grain size at the Woods Hole Science Center. USGS Open-File Report 2005-1001 (Poppe et al., 2005), available on DVD and online, describes the field methods used to collect marine sediment samples as well as the laboratory methods used to determine and characterize grain-size distributions, and presents these data in several formats that can be readily employed by interested parties. The report is divided into three sections. The first section discusses procedures and contains pictures of the equipment, analytical flow diagrams, video clips with voice commentary, classification schemes, useful forms and compiled and uncompiled versions of the data-acquisition and data-processing software with documentation. The second section contains the grain-size data for more than 23,000 analyses in two “flat-file” formats, a data dictionary, and color-coded browse maps. The third section provides a GIS data catalog of the available point, interpretive, and baseline data layers, with FGDC-compliant metadata to help users visualize the textural information in a geographic context.

Conference Paper↗
USGS2016

Statistical analysis and mapping of water levels in the Biscayne aquifer, water conservation areas, and Everglades National Park, Miami-Dade County, Florida, 2000–2009

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000–2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000–2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990–1999 and 2000–2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974–2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990–1999 and 2000–2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990–1999 and 2000–2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000–2009 than during 1990–1999; and that inland water levels were generally lower during 2000–2009 than during 1990–1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990–1999. Mean October water levels during 2000–2009 were generally higher than during 1990–1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Florida↗
USGS2012

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Khanneshin mineral district in Afghanistan: Chapter A 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 Khanneshin mineral district, which has uranium, thorium, rare-earth-element, and apatite 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 Khanneshin) and the WGS84 datum. The final image mosaics were subdivided into nine overlapping tiles or quadrants because of the large size of the target area. The nine image tiles (or quadrants) for the Khanneshin 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 Khanneshin study area, one subarea was designated for detailed field investigations (that is, the Khanneshin volcano subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.

Helm;Nimroz↗
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↗
USGS2012

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Aynak mineral district in Afghanistan: Chapter E 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 Aynak 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 ((c)JAXA,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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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 Aynak) 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 Aynak 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 Aynak study area, five subareas were designated for detailed field investigations (that is, the Bakhel-Charwaz, Kelaghey-Kakhay, Kharuti-Dawrankhel, Logar Valley, and Yagh-Darra/Gul-Darra subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Kabul;Logar;Paktya;Wardak↗
USGS2012

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Badakhshan mineral district in Afghanistan: Chapter F 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 Badakhshan 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 ((c)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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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 Badakhshan) and the WGS84 datum. The final image mosaics were subdivided into six overlapping tiles or quadrants because of the large size of the target area. The six image tiles (or quadrants) for the Badakhshan 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 Badakhshan study area, three subareas were designated for detailed field investigations (that is, the Bharak, Fayz-Abad, and Ragh subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Badakhshan↗
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↗
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↗