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At least 271 records · Page 15Linked to original sources
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↗
USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Herat mineral district in Afghanistan: Chapter T 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 Herat mineral district, which has barium and limestone 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,2007,2008,2009), 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 1,000-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 Herat) 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 Herat 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 Herat study area, one subarea was designated for detailed field investigations (that is, the Barium-Limestone subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.

Herat↗
USGS2017

Multiscale guidance and tools for implementing a landscape approach to resource management in the Bureau of Land Management

The Bureau of Land Management (BLM) is implementing a landscape approach to resource management (hereafter, landscape approach) to more effectively work with partners and understand the effects of management decisions. A landscape approach is a set of concepts and principles used to guide resource management when multiple stakeholders are involved and goals include diverse and sustainable social, environmental, and economic outcomes. Core principles of a landscape approach include seeking meaningful participation of diverse stakeholders, considering diverse resource values in multifunctional landscapes, acknowledging the tradeoffs needed to meet diverse objectives in the context of sustainable resource management, and addressing the complexity of social and ecological processes by embracing interdisciplinarity and considering multiple and broad spatial and temporal perspectives. In chapter 1, we outline the overall goal of this report: to provide a conceptual foundation and framework for implementing a landscape approach to resource management in the BLM, focusing on the role of multiscale natural resource monitoring and assessment information. In chapter 2, we describe a landscape approach to resource management. BLM actions taken to implement a landscape approach include a major effort to compile broad-scale data on natural resource status and condition across much of the west. These broadscale data now provide a regional context for interpreting monitoring data collected at individual sites and informing decisions made for local projects. We also illustrate the utility of using multiscale data to understand potential effects of different resource management decisions, define relevant terms in landscape ecology, and identify spatial scales at which planning and management decisions may be evaluated. In chapter 3, we describe how the BLM Rapid Ecoregional Assessment program and Assessment, Inventory and Monitoring program may be integrated to provide the multiscale monitoring data needed to inform a landscape approach. We propose six core, broad-scale indicators of natural resource status and condition: the amount, spatial distribution, patch size and connectivity of ecosystems and wildlife habitats, and the pattern of existing development across the landscape. Additional supplemental broad-scale indicators may include fire return intervals, distributions of invasive species, and vulnerability of ecosystems to a changing climate. Landscape intactness is an additional derived indicator that is calculated from one or more of the core and supplemental broad-scale indicators. We then outline a process for assessing broad-scale indicators that is consistent with the overall BLM Assessment, Inventory, and Monitoring process, facilitating development of a multiscale natural resource monitoring program. Finally, we describe how broad-scale indicators of natural resource status and condition may guide field monitoring implemented through the BLM Assessment, Inventory and Monitoring program and help address complex management questions. In chapter 4, we consider the specific question of assessing the ecological integrity of rangelands across the western United States. We first define ecological integrity and its relation to land health. We then suggest that a combination of six local-scale indicators collected through field sampling at individual sites and five complementary broad-scale indicators together provide information on the composition, structure, and function of rangelands. The terrestrial monitoring indicators collected at the level of individual field sites are the amount of bare ground, vegetation composition (including invasive plants and plants of management concern), vegetation height, and the proportion of the soil surface in large intercanopy gaps. The broad-scale indicators are vegetation amount, distribution, patch size, connectivity, and productivity, along with the pattern of terrestrial development. Our suggested approach to quantifying ecological integrity focuses specifically on informing management of public lands for multiple resource uses, and illustrates how existing data collected through BLM monitoring and assessment programs may be used together to provide multiscale information on land condition across broad extents. In chapter 5, we develop a method for quantifying landscape intactness and apply this method to the western United States. Our multiscale index of landscape intactness is designed to be defensible, decomposable, and easy to understand. The foundation of the multiscale index of landscape intactness is the surface disturbance footprint of anthropogenic development, including energy and urban development, roads and railroads, cultivated croplands, surface mines and quarries, and energy transmission lines and pipelines. The index represents a gradient of anthropogenic influence as represented by development summarized at two spatial scales of analysis: 2.5 and 20 kilometers. We provide several example applications of the index, illustrating how these data may inform natural resource decisions at the spatial extent of BLM field and district offices, states, ecoregions, and the western United States. We find that 19.2 percent of lands managed by the BLM across the 17 western states of the conterminous United States had the highest landscape intactness. The largest intact areas occur on public lands at high elevations or in the Great Basin. We believe the frameworks, processes, and analyses provided in this report will improve the ability of the BLM to identify and evaluate potential direct and indirect effects of management actions (such as habitat restoration and renewable energy development), and assist the BLM in further implementing a landscape approach to resource management.

Open-File Report↗
USGS2024

Development and calibration of HEC–RAS hydraulic, temperature, and nutrient models for the Mohawk River, New York

In support of a preliminary analysis performed by New York State Department of Environmental Conservation that found elevated nutrient levels along selected reaches of the Mohawk River, a one-dimensional, unsteady hydraulic and water-quality model (Hydrologic Engineering Center River Analysis System Nutrient Simulation Module 1 [HEC–RAS NSM I]) was developed by the U.S. Geological Survey for the 127-mile reach of the Mohawk River between Rome and Cohoes, New York. The model was designed to accurately simulate within-channel flow conditions for this highly regulated, control-structure dense river reach. The model was calibrated for the period of May through September 2016 using available streamflow, temperature, and water-quality data. Nitrogen, phosphorus, dissolved oxygen, and water column algae were balanced within the model; however, the nutrient model calibration was focused on phosphorus. The HEC–RAS hydraulic model simulated streamflow adequately at the calibration locations with observed and simulated daily flows demonstrating coefficient of determination ( r 2 ) values ranging from 0.91 to 0.97, mean absolute error ranging from 15–20 percent, and bias ranging from −7 to 16 percent. The water temperature model within HEC–RAS NSM I demonstrated remarkable ability to simulate water temperature: typical water temperature errors were less than 1.0 degree Celsius (°C). Simulated water temperature results closely tracked observed continuous water temperature data at three locations on the Mohawk River, with mean absolute error for the 2016 study period ranging from 0.87 to 0.90 °C, and a root mean square error of 1.00 to 1.07 °C. Performance criteria for the water-quality (nutrient) model were applied differently than the water temperature model because of the temporally coarse discrete samples collected for the project. The average difference between final simulated concentrations and observed concentrations of organic phosphorus for all sample locations was within 0.01 milligrams per liter (mg/L) and within 0.09 mg/L for orthophosphate using all locations except Rome, which was within 0.25 mg/L. The calibrated model was used to implement nine phosphorus reduction scenarios by applying reductions to wastewater treatment plant effluent concentrations within the model. Monthly mean differences were computed for five comparison locations. Scenario results were generally linear and predictable; scenarios implementing the highest reductions showed correspondingly larger differences in Mohawk River concentrations downstream from the wastewater treatment plants associated with those reductions. The largest monthly mean differences were realized from reduction scenario nine and ranged from −0.018 to −0.076 mg/L for organic phosphorus and from 0.001 to −0.138 mg/L for orthophosphate.

New York↗
USGS2006

Dissemination of LANDFIRE Prototype Project data

The transfer of LANDFIRE data to users is the most important aspect of the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE Prototype Project). The creation of an accurate, consistent, nationwide data set provides the foundation for a successful project. The final step is to make the data readily available to the user community. User capabilities and needs vary widely. Many users require LANDFIRE data to solve day-to-day wildfire management problems such as planning fuel treatments or managing active wildfires. Others use LANDFIRE data to gather information over large geographic areas for strategic planning and analyses. The diversity of users and the variety of applications of LANDFIRE data present an interesting challenge: to develop a data dissemination system that is comprehensive, user-friendly, and flexible. The system must be functional across many levels of technology, ranging from powerful computing capability to support national-scale strategic planning to field-level tactical wildfire operations support. The system must be sustainable, dependable, affordable, and adaptable to various levels of and changes in technology.

General Technical Report↗
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↗
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↗
USGS1996

Numerical simulation of solute transport in southwestern Salt Lake Valley, Utah

Contaminated ground water characterized by high concentrations of dissolved solids and dissolved sulfate, and in areas, by low pH and elevated concentrations of metals, is present near public-supply wells in the southwestern Salt Lake Valley. To provide State officials and water users with information concerning the potential movement of contaminated ground water to points of withdrawal in the area, an analysis of solute transport using computer models was done by the U.S. Geological Survey in cooperation with the Utah Department of Natural Resources, Division of Water Rights, and local municipalities and water users. A three-dimensional solute-transport model was developed and couples with an existing ground-water flow model of Salt Lake Valley to simulate the movement of dissolved sulfate in ground water in southwestern Salt Lake Valley. Development and calibration of the transport model focused mainly on sulfate movement down-gradient from the Bingham Creek Reservoirs and the South Jordan evaporation ponds east of the mouth of Bingham Canyon. Estimates of transport parameters were adjusted during a calibration simulation representing conditions during 1965-93. After calibration, the transport model was used to simulate future sulfate movement for 1994-2043. Because of uncertainty in estimated transport-parameter values, three projection transport simulations incorporating a range of probable parameter values were done to evaluate future sulfate movement and changes in sulfate concentrations at selected public-supply wells. These projection simulations produced a possible range of computed transport rates and patterns. In general, the projection simulations indicated movement of the sulfate plume east of the Bingham Creek reservoir toward public-supply wells northeast of the reservoirs and then eastward toward the Jordan River. Ground water with high concentrations of sulfate east of the South Jordan evaporation ponds is simulated as moving west to east under the Jordan River towards public-supply wells during the final 25 years of the simulation period. An increase in sulfate concentration from 200 mg/l in 2006 to 4,100 mg/l in 2022 was the largest simulated increase at public-supply wells northeast of the reservoirs. An increase in sulfate concentration from 150 mg/l in 2024 to 340 mg/l in 2043 was the largest simulated increase at public-supply wells in the south-central Salt Lake Valley just east of the Jordan River.

Utah↗
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↗
USGS2013

Status of a reconnaissance field study of the Susitna basin, 2011

The Alaska Division of Geological & Geophysical Surveys (DGGS) and Alaska Division of Oil and Gas (DOG), in collaboration with the U.S. Geological Survey (USGS) performed reconnaissance field studies for ten days in late June 2011, in the Susitna basin, directly north of Cook Inlet, south-central Alaska (fig. 1). The purpose of our investigation was to reconnoiter outcrops in the basin and along its periphery to gather new information towards understanding the basin formation history and stratigraphy. This reconnaissance data represents the first step toward better understanding the basin’s hydrocarbon potential, a key component of DGGS’s multi-year In- State Gas Program. This program is focused on collecting baseline geologic information from potential frontier gas basins to encourage new exploration to help, in part, reduce the high cost of energy in rural Alaska. Our work represents the first season of this three-year project. Preliminary results from year two, a companion project within the Nenana and Tanana basins in interior Alaska, are described by Wartes and others (2013). DGGS plans to return to the Susitna basin for follow-up fieldwork during the third and final year of the program. The motivation for developing a better understanding of the Susitna basin stems from the recognition that the Susitna basin shares similar age coal-bearing strata with the adjacent, petroliferous Cook Inlet forearc basin (Barnes, 1966; Reed and Nelson, 1980) and with exhumed strata in the Matanuska Valley forearc basin (Trop and others, 2003) (figs. 1 and 2). Cook Inlet basin has eight producing oil fields, more than 25 producing gas fields, and likely contains many additional undiscovered oil and gas accumulations (LePain and others, in press). Most of the Cook Inlet gas is of microbial origin and apparently was sourced from abundant coalbeds of primarily Miocene age in the Tyonek, Beluga, and Sterling Formations (Claypool and others, 1980; Magoon, 1994). If the biogenic gas model for Cook Inlet is applicable to the Susitna basin, then the latter may be a viable source for Alaska Railbelt and rural energy needs. This brief overview report summarizes the reconnaissance field data collected in the Susitna basin during the first summer of the program. As the data are developed, this report will be followed by interpretive technical reports addressing the stratigraphy, reservoir quality, coal quality and gas potential, hydrocarbon seal integrity, subsurface structure, and uplift history of the basin and sub-basin margins.

Alaska↗
USGS2022

Potential effects of climate change on snail kites (Rostrhamus sociabilis plumbeus) in Florida

The snail kite ( Rostrhamus sociabilis plumbeus ), an endangered, wetland-dependent raptor, is highly sensitive to changes in hydrology. Climate-driven changes in water level will likely affect snail kite populations—altering reproductive success and survival rates. Identifying the mechanisms mediating the direct and indirect effects of climate on snail kite populations and the range of future climate conditions is important to the conservation of this species. When water levels are low, snail kite nest initiation and nest success decrease owing to decreased availability of their primary prey applesnails ( Pomacea spp.), unstable nesting sites, and increased predator access. Dry events also lead to decreased adult and juvenile survival. In the next 80 years, temperatures and potential evapotranspiration are projected to increase in central and southern Florida. Although future precipitation volume is more uncertain, increased temperatures and evaporative loss may lead to increased frequency, duration, and severity of low-water events. Additionally, rapidly rising water levels have adverse effects on snail kite reproductive success—destroying nests, preventing access to apple snails, and reducing apple snail productivity. Finally, it is likely that future climate will favor more frequent dry conditions and extreme heavy rainfall events, both of which are directly linked to decreased reproductive success and survival. The potential effects of climate change may be buffered by the availability of alternative prey (non-native applesnails) that are more tolerant of anticipated conditions. In highly controlled southern Florida waterbodies, regional water-management decisions may buffer or exacerbate waterbody accession and recession rates.

Florida↗
USGS2005

NOAA-USGS Debris-Flow Warning System - Final Report

Landslides and debris flows cause loss of life and millions of dollars in property damage annually in the United States (National Research Council, 2004). In an effort to reduce loss of life by debris flows, the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) and the U.S. Geological Survey (USGS) operated an experimental debris-flow prediction and warning system in the San Francisco Bay area from 1986 to 1995 that relied on forecasts and measurements of precipitation linked to empirical precipitation thresholds to predict the onset of rainfall-triggered debris flows. Since 1995, there have been substantial improvements in quantifying precipitation estimates and forecasts, development of better models for delineating landslide hazards, and advancements in geographic information technology that allow stronger spatial and temporal linkage between precipitation forecasts and hazard models. Unfortunately, there have also been several debris flows that have caused loss of life and property across the United States. Establishment of debris-flow warning systems in areas where linkages between rainfall amounts and debris-flow occurrence have been identified can help mitigate the hazards posed by these types of landslides. Development of a national warning system can help support the NOAA-USGS goal of issuing timely Warnings of potential debris flows to the affected populace and civil authorities on a broader scale. This document presents the findings and recommendations of a joint NOAA-USGS Task Force that assessed the current state-of-the-art in precipitation forecasting and debris-flow hazard-assessment techniques. This report includes an assessment of the science and resources needed to establish a demonstration debris-flow warning project in recently burned areas of southern California and the necessary scientific advancements and resources associated with expanding such a warning system to unburned areas and, possibly, to a national scope.

Circular↗
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↗
USGS2020

Liquefaction and related ground failure from July 2019 Ridgecrest earthquake sequence

The 2019 Ridgecrest earthquake sequence produced a 4 July M 6.5 foreshock and a 5 July M 7.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24 hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non‐ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar‐based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre‐ and postevent mapping of liquefaction hazards. We describe liquefaction and ground‐failure features in Trona and Argus, which produced lateral deformations and impacts on several single‐story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction‐related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.

California↗
USGS2020

Geophysical surveys, hydrogeologic characterization, and groundwater flow model for the Truxton basin and Hualapai Plateau, northwestern Arizona

This is the third and final report in a series that describe the groundwater resources of the Hualapai Indian Reservation. These reports document the findings of a comprehensive groundwater study conducted on the reservation and adjacent areas from 2015 through 2018 by the U.S. Geological Survey in cooperation with the Bureau of Reclamation. The first report described the hydrologic framework and characterization of the Truxton aquifer on the Hualapai Indian Reservation (Bills and Macy, 2016). The second report described the hydrogeologic characterization of the Hualapai Plateau part of the reservation (Mason, Macy, and others, 2020). This report includes five chapters. Chapter A (Mason, Knight, and others, 2020) is a summary of this multichapter volume and briefly describes the study area. Chapter B (Mason, Bills, and Macy, 2020) describes the geology and hydrology of the Truxton basin and Hualapai Plateau. Chapter C (Kennedy, 2020) describes the results of a gravity geophysical survey of the Truxton basin. Chapter D (Ball, 2020) describes the findings of an airborne electromagnetic survey of the Truxton aquifer and Hualapai Plateau. Chapter E (Knight, 2020) describes the results of a transient groundwater model created for the entire Truxton aquifer both on and off the reservation. The groundwater-flow model is used to estimate projected groundwater levels based on future groundwater withdrawal scenarios.

Arizona↗
USGS2012

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dusar-Shaida mineral district in Afghanistan: Chapter I 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 Dusar-Shaida mineral district, which has copper and tin 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,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’ 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’ local zone (41 for Dusar-Shaida) 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 Dusar-Shaida 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 Dusar-Shaida study area, three subareas were designated for detailed field investigations (that is, the Dahana-Misgaran, Kaftar VMS, and Shaida subareas); these subareas were extracted from the area’ image mosaic and are provided as separate embedded geotiff images.

Farah;Herat↗
USGS2014

Land-margin ecosystem hydrologic data for the coastal Everglades, Florida, water years 1996-2012

Mangrove forests and salt marshes dominate the landscape of the coastal Everglades (Odum and McIvor, 1990). However, the ecological effects from potential sea-level rise and increased water flows from planned freshwater Everglades restoration on these coastal systems are poorly understood. The National Park Service (NPS) proposed the South Florida Global Climate Change Project (SOFL-GCC) in 1990 to evaluate climate change and the effect from rising sea levels on the coastal Everglades, particularly at the marsh/mangrove interface or ecotone (Soukup and others, 1990). A primary objective of SOFL-GCC project was to monitor and synthesize the hydrodynamics of the coastal Everglades from the upstream freshwater marsh to the downstream estuary mangrove. Two related hypotheses were set forward (Nuttle and Cosby, 1993): 1. There exists hydrologic conditions (tide, local rainfall, and upstream water deliveries), which characterize the location of the marsh/mangrove ecotone along the marine and terrestrial hydrologic gradient; and 2. The marsh/mangrove ecotone is sensitive to fluctuations in sea level and freshwater inflow from inland areas. Hydrologic monitoring of the SOFL-GCC network began in 1995 after startup delays from Hurricane Andrew (August 1992) and organizational transfers from the NPS to the National Biological Survey (October 1993) and the merger with the U.S. Geological Survey (USGS) Biological Research Division in 1996 (Smith, 2004). As the SOFL-GCC project progressed, concern by environmental scientists and land managers over how the diversion of water from Everglades National Park would affect the restoration of the greater Everglades ecosystem. Everglades restoration scenarios were based on hydrodynamic models, none of which included the coastal zone (Fennema and others, 1994). Modeling efforts were expanded to include the Everglades coastal zone (Schaffranek and others, 2001) with SOFL-GCC hydrologic data assisting the ecological modeling needs. In 2002, as a response for a more interdisciplinary science approach to understanding the coastal Everglades ecological system, the SOFL-GCC hydrology project was integrated into the “Dynamics of Land-Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate” study (Smith and others, 2002). Data from the ongoing study has been useful in providing an empirical hydrologic baseline for the greater Everglades ecosystem restoration science and management needs. The hydrology network consisted of 13 hydrologic gaging stations installed in the southwestern coastal region of Everglades National Park along three transects: Shark River (Shark or SH) transect, Lostmans River (Lostmans or LO) transect, and Chatham River (Chatham or CH) transect (fig. 1). There were five paired surface-water/groundwater gaging stations on the Shark transect (SH1, SH2, SH3, SH4, and SH5) and one stage gaging station (BSC) in the Big Sable Creek; four paired surface-water/groundwater gaging stations on the Lostmans transect (LO1, LO2, LO3, and LO4); and three paired surface-water/groundwater gaging stations on the Chatham transect (CH1, CH2, and CH3). Both surface-water and groundwater levels, salinities, and temperatures were monitored at the paired gaging stations. Rainfall was recorded at marsh and open canopy gaging stations. This report details the study introduction, method, and description of data collected, which are accessible through the final instantaneous hydrologic dataset stored in the USGS South Florida Information Access (SOFIA) South Florida Hydrology Database website, http://sofia.usgs.gov/exchange/sfl_hydro_data/location.html#brdlandmargin .

Florida↗
USGS2020

Final project memorandum: Southeast Climate Adaptation Science Center Project

Low-lying public lands along the northern Gulf of Mexico coast are vulnerable to sea-level rise. Coastal planners and resource managers in the region have requested customized information that can be used to concisely communicate local sea-level rise scenarios and identify potential impacts to the missions of management agencies. In this project, researchers worked with the Northern Gulf of Mexico Sentinel Site Cooperative to develop information sheets outlining potential sea-level rise scenarios for the region through 2100 including their potential impact on the missions of federally-managed lands. Researchers drew from existing information on regional and global sea-level rise scenarios to develop customized information sheets for 54 federally-managed lands in the region (e.g., National Wildlife Refuges, National Park Service lands, National Estuarine Research Reserves) (Chivoiu et al. 2020). This project was developed in response to a request for customized sea-level rise scenario products from partners at the Northern Gulf of Mexico Sentinel Site Cooperative and the U.S. Fish and Wildlife Service’s Gulf Restoration Program. The results of this project support the conservation stewardship missions of the U.S. Fish and Wildlife Service and National Park Service by providing science to inform management of their lands along the Gulf of Mexico.

Alabama, Florida, Louisiana, Mississippi, Texas↗