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USGS2018

The natural capital accounting opportunity: Let's really do the numbers

The nation’s economic accounts provide objective, regular, and standardized information routinely relied upon by public and private decision makers. But they are incomplete. The U.S. and many other nations currently do not account for the natural capital — such as the wildlife, forests, grasslands, soils, and water bodies—upon which all other economic activity rests. By creating formal natural capital accounts (NCA) and ecosystem goods and services (EGS) accounts, governments and businesses can better understand the past, peer into the future, innovate, conserve, and plan for environmental shocks. They would standardize, regularly repeat, and aggregate diverse natural resource, environmental, and social and economic data and could thereby play a significant role in advancing the science of coupled biophysical and social systems.

BioScience↗
USGS2022

A practical guide to understanding and validating complex models using data simulations

Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood-based approaches (e.g. packages lme4 ) and machine learning methods. These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution , and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods. Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution . We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement. Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.

Methods in Ecology and Evolution↗
USGS2011

Some possible causes of and corrections for STS-1 response changes in the Global Seismographic Network

The Global Seismographic Network (GSN) (Figure 1) plays a key role in providing seismic data for global earthquake monitoring ( e.g. , Benz et al. 2005), earthquake science ( e.g. , Tsai et al. 2005), and studies of Earth structure ( e.g. , Dalton et al. 2008). One of the key GSN design goals is to "provide high fidelity digital recordings of all teleseismic ground motions (adequate to resolve at or near ambient noise up to the largest teleseismic signals over the bandwidth from free oscillations (10 -4 Hz) to teleseismic body waves (up to approximately 15 Hz))" (GSN ad hoc Design Goals Subcommittee 2002). To help meet this goal, Streckeisen STS-1 seismometers were deployed at 80 GSN stations. Some of the GSN sensors have been deployed for more than 25 years. Several recent studies (Davis et al. 2005; Ekström et al. 2006; Davis and Berger 2007) have examined the question of overall calibration of the GSN. Ekström et al. (2006) indicated that a number of sites showed anomalous responses and suggested a gradual decay in the sensitivity. We have investigated the anomalous responses at several GSN sites. At least some of the problems observed by Ekström et al. (2006) may be attributed to humid air leaking into the feedback electronics of the STS-1 seismometers, which produces lower than normal sensitivities near the long-period corner of the instrument (360 seconds period). It appears that even though the feedback electronics boxes are designed to be sealed, water vapor can penetrate their interior after they have been exposed to highly humid seismometer vault air for extended periods. Highly humid air was also found to be present inside some STS-1 bell-jars (especially horizontal instruments) after loss of vacuum, resulting in corrosion and leakage between electrical conductors in connectors.

Seismological Research Letters↗
USGS1981

Marine turtles of the Galapagos Islands and adjacent areas of the eastern Pacific on the basis of observations made by J.R. Slevin 1905-1906

The field notes of J. R. Slevin written during the expedition of the California Academy of Sciences to the Galapagos Islands in 1905-1096 contain previously unavailable data on the marine turtles of the eastern Pacific. 'Land basking' by green turtles in Galapagos was predominately, if not exclusively, a female behavior. These terrestrial emergences were not concentrated in the major reproductive season of Galapagos turtles. Female Chelonia were also collected on shore during daylight hours on Socorro Island southwest of Baja California, Mexico. Chelonia, green turtles, were observed to feed on seaweed, the leaves and shoots of mangrove trees, and the leaves of another unidentified shoreline shrub. Comparative data on the gonads of dark and yellow turtles indicated that the latter did not breed in Galapagos during Slevin's stay. Lepidochelys olivacea, the olive ridley, was recorded in Galapagos waters and fed on fish eggs

Journal of Herpetology↗
USGS2014

Great Lakes prey fish populations: a cross-basin overview of status and trends based on bottom trawl surveys, 1978-2013

The assessment of Great Lakes prey fish stocks have been conducted annually with bottom trawls since the 1970s by the Great Lakes Science Center, sometimes assisted by partner agencies. These stock assessments provide data on the status and trends of prey fish that are consumed by important commercial and recreational fishes. Although all these annual surveys are conducted using bottom trawls, they differ among the lakes in the proportion of the lake covered, seasonal timing, trawl gear used, and the manner in which the trawl is towed (across or along bottom contours). Because each assessment is unique, population indices were standardized to the highest value for a time series within each lake for the following prey species: Cisco (Coregonus artedi), Bloater (C. hoyi), Rainbow Smelt (Osmerus mordax), Alewife (Alosa pseudoharengus), and Round Goby (Neogobius melanostomus). In this report, standardized indices are presented in graphical form along with synopses to provide a short, informal cross-basin summary of the status and trends of principal prey fishes. There was basin-wide agreement in the trends of age-1 and older biomass for all prey species, with the highest concordance occurring for coregonids and Rainbow Smelt, and weaker concordance for Alewife. For coregonids, the highest biomass occurred from the mid-1980s to the mid-1990s. Rainbow Smelt biomass declined slowly and erratically during the last quarter century. Alewife biomass was generally higher from the early 1980s through 1990s across the Great Lakes, but since the early 1990s, trends have been divergent across the lakes, though there has been a downward trend in all lakes since 2005. Recently, Lake Huron has shown resurgence in biomass of Bloater, achieving 75% of its maximum record in 2012 due to recruitment of a succession of strong and moderate year classes that appeared in 2005-2011. Also, strong recruitment of the 2010 year class of Alewife has led to a sharp increase in biomass of Alewife in Lake Michigan. In general, trends in year-class strengths were less concordant across the basin and only coregonids showed statistical agreement across the upper Great Lakes. The appearance of strong and moderate year-classes of Bloater in Lake Huron in 2005- 2011 countered the trend of continuing weak year-classes of coregonids in Lakes Michigan and Superior. Not shown in our analysis is the appearance of the 2013 year-class of Bloater in Huron, the largest to date. There was no agreement in cross-basin trends in year-class strengths for Rainbow Smelt and Alewife, although there was agreement between pairs of lakes. Although there was statistical agreement in trends of age-0 and older Round Goby biomass among lakes where this species has successfully invaded (Michigan, Huron, Erie and Ontario), temporal patterns of biomass in each lake were different. Round Goby may be approaching equilibrium in Lake Erie, peaking in Lake Huron, and expanding in Lake Michigan. The trend in Lake Ontario remains unclear. Declining abundance in Lake Erie has corresponded with evidence that Round Goby have become increasingly incorporated into piscivore diets, e.g., Lake Trout, Walleye, Smallmouth Bass, Yellow Perch, and Burbot in Lakes Michigan, Huron, Erie, and Ontario. Round Goby continue to be absent from spring bottom trawl assessments in Lake Superior, but their presence in the harbors and embayments of Duluth and Thunder Bay (U.S. Geological Survey and Ontario Ministry of Natural Resources, unpublished data), suggests that there is potential for future colonization.

Great Lakes↗
USGS2024

The U.S. Geological Survey Ohio Water Microbiology Laboratory

The U.S. Geological Survey Ohio Water Microbiology Laboratory is a part of the Ohio-Kentucky-Indiana Water Science Center. The mission of the laboratory is to provide microbiological data of public health significance from surface waters, groundwaters, and sediments for a variety of study objectives. The laboratory conducts internal projects, works with external cooperators, and assists U.S. Geological Survey offices and National programs. The laboratory offers guidance, study design, and data interpretation expertise to collaborators, all following rigorous quality control and quality assurance procedures.

Fact Sheet↗
USGS2008

Basic Statistical Concepts and Methods for Earth Scientists

INTRODUCTION Statistics is the science of collecting, analyzing, interpreting, modeling, and displaying masses of numerical data primarily for the characterization and understanding of incompletely known systems. Over the years, these objectives have lead to a fair amount of analytical work to achieve, substantiate, and guide descriptions and inferences.

Open-File Report↗
USGS2009

U.S. Geological Survey Global Seismographic Network - Five-Year Plan 2006-2010

The Global Seismographic Network provides data for earthquake alerting, tsunami warning, nuclear treaty verification, and Earth science research. The system consists of nearly 150 permanent digital stations, distributed across the globe, connected by a modern telecommunications network. It serves as a multi-use scientific facility and societal resource for monitoring, research, and education, by providing nearly uniform, worldwide monitoring of the Earth. The network was developed and is operated through a partnership among the National Science Foundation (http://www.nsf.gov), the Incorporated Research Institutions for Seismology (http://www.iris.edu/hq/programs/gsn), and the U.S. Geological Survey (http://earthquake.usgs.gov/gsn).

Open-File Report↗
USGS2005

ShakeMap manual: technical manual, user's guide, and software guide

ShakeMap (http://earthquake.usgs.gov/shakemap) --rapidly, automatically generated shaking and intensity maps--combines instrumental measurements of shaking with information about local geology and earthquake location and magnitude to estimate shaking variations throughout a geographic area. The results are rapidly available via the Web through a variety of map formats, including Geographic Information System (GIS) coverages. These maps have become a valuable tool for emergency response, public information, loss estimation, earthquake planning, and post-earthquake engineering and scientific analyses. With the adoption of ShakeMap as a standard tool for a wide array of users and uses came an impressive demand for up-to-date technical documentation and more general guidelines for users and software developers. This manual is meant to address this need. ShakeMap, and associated Web and data products, are rapidly evolving as new advances in communications, earthquake science, and user needs drive improvements. As such, this documentation is organic in nature. We will make every effort to keep it current, but undoubtedly necessary changes in operational systems take precedence over producing and making documentation publishable.

Techniques and Methods↗
USGS2012

Great lakes prey fish populations: a cross-basin overview of status and trends based on bottom trawl surveys, 1978-2012

The assessment of prey fish stocks in the Great Lakes have been conducted annually with bottom trawls since the 1970s by the Great Lakes Science Center, sometimes assisted by partner agencies. These stock assessments provide data on the status and trends of prey fish that are consumed by important commercial and recreational fishes. Although all these annual surveys are conducted using bottom trawls, they differ among the lakes in the proportion of the lake covered, seasonal timing, bottom trawl gear used, and the manner in which the trawl is towed (across or along bottom contours). Because each assessment is unique in one or more important aspects, direct comparison of prey fish catches among lakes is not straightforward. However, all of the assessments produce indices of abundance or biomass that can be standardized to facilitate comparisons of status and trends across all the Great Lakes. In this report, population indices were standardized to the highest value for a time series within each lake for the following principal prey species: cisco ( Coregonus artedi ), bloater ( C. hoyi ), rainbow smelt ( Osmerus mordax ), and alewife ( Alosa pseudoharengus ). Indices were also provided for round goby ( Neogobius melanostomus ), an invasive fish that has proliferated throughout the basin over the past 18 years. These standardized indices represent the best available long-term indices of relative abundance for these fishes across all of the Great Lakes. In this report, standardized indices are presented in graphical form along with synopses to provide a short, informal cross-basin summary of the status and trends of principal prey fishes. In keeping with this intent, tables, references, and a detailed discussion were omitted.

Great Lakes↗
USGS2015

Great Lakes prey fish populations: A cross-basin Overview of status and trends based on bottom trawl surveys, 1978-2014

The assessment of Great Lakes prey fish stocks have been conducted annually with bottom trawls since the 1970s by the Great Lakes Science Center, sometimes assisted by partner agencies. These stock assessments provide data on the status and trends of prey fish that are consumed by important commercial and recreational fishes. Although all these annual surveys are conducted using bottom trawls, they differ among the lakes in the proportion of the lake covered, seasonal timing, trawl gear used, and the manner in which the trawl is towed (across or along bottom contours). Because each assessment is unique, population indices were standardized to the highest value for a time series within each lake for the following prey species: Cisco (Coregonus artedi), Bloater (C. hoyi), Rainbow Smelt (Osmerus mordax), Alewife (Alosa pseudoharengus), and Round Goby (Neogobius melanostomus). In this report, standardized indices are presented in graphical form along with synopses to provide a short, informal cross-basin summary of the status and trends of principal prey fishes.

Report↗
USGS2013

Community-based water-quality monitoring in the Yukon River Basin and the Kuskokwim Watershed

The unique partnership between the USGS and the YRITWC provides mutual benefits by fostering outreach efforts that have been essential for community empowerment and by generating scientific data for prohibitively large and remote regions that would be challenging for USGS scientists to sample as robustly alone. The addition of a new partnership with the KRWC to create a community-based monitoring program will only increase these benefits by growing the spatial extent of data collection and empowering more people to take charge of important science in their own backyard.

Alaska, Yukon↗
USGS2017

Nutrients, phytoplankton, zooplankton, and macrobenthos

Lower trophic levels support the prey fish on which most sport fish depend. Therefore, understanding the production potential of lower trophic levels is integral to the management of Lake Ontario’s fishery resources. Lower trophic-level productivity differs among offshore and nearshore waters. In the offshore, there is concern about the ability of the lake to support Alewife (Table 1) production due to a perceived decline in productivity of phytoplankton and zooplankton whereas, in the nearshore, there is a concern about excessive attached algal production (e.g., Cladophora) associated with higher nutrient concentrations—the oligotrophication of the offshore and the eutrophication of the nearshore (Mills et al. 2003; Holeck et al. 2008; Dove 2009; Koops et al. 2015; Stewart et al. 2016). Even though the collapse of the Alewife population in Lake Huron in 2003 (and the associated decline in the Chinook Salmon fishery) may have been precipitated by a cold winter (Dunlop and Riley 2013), Alewife had not returned to high abundances in Lake Huron as of 2014 (Roseman et al. 2015). Failure of the Alewife population to recover from collapse has been attributed to declines in lower trophic-level production (Barbiero et al. 2011; Bunnell et al. 2014; but see He et al. 2015). In Lake Michigan, concerns of a similar Alewife collapse led to a decrease in the number of Chinook Salmon stocked. If lower trophic-level production declines in Lake Ontario, a similar management action could be considered. On the other hand, in Lake Erie, which supplies most of the water in Lake Ontario, eutrophication is increasing and so are harmful algal blooms. Thus, there is also a concern that nutrient levels and algal blooms could increase in Lake Ontario, especially in the nearshore. Solutions to the two processes of concern—eutrophication in the nearshore and oligotrophication in the offshore—may be mutually exclusive. In either circumstance, fisheries management needs information on the productivity of lower trophic levels in Lake Ontario. In this chapter, we review the status of lower trophic levels in Lake Ontario with special attention to the current (2008-2013) and previous (2003-2007) reporting periods. During the two reporting periods, three whole-lake surveys of lower trophic levels were conducted: the Lower Trophic Level Assessment (LOLA) in 2003 and 2008 (Makarewicz and Howell 2012; Munawar et al. 2015b) and the Cooperative Science and Management Initiative (CSMI) in 2013. Analyses of the CSMI data are ongoing. In addition to the three one-year sources of information on lower trophic levels, several multi-year sources of information are available, including data from the surveillance program conducted since 1965 by Environment Canada (EC) (Dove 2009), monitoring conducted since 1980 by the U.S. Environmental Protection Agency’s (EPA) Great Lakes National Program Office (GLNPO) (Barbiero et al. 2014; Reavie et al. 2014), sampling for a Bioindex Program at two stations, one offshore and one in the Eastern Basin, assessments of Mysis diluviana (formerly Mysis relicta) conducted since 1980 by Fisheries and Oceans Canada (Johannsson et al. 1998, 2011) and the Ontario Ministry of Natural Resources and Forestry (OMNRF), and monitoring conducted since 1995 by the Biomonitoring Program (BMP) on the New York side of the lake (Holeck et al. 2015b). The BMP is a collaboration of the New York State Department of Environmental Conservation (DEC), U.S. Fish and Wildlife Service, U.S. Geological Survey (USGS), and Cornell University.

Special Publication↗
USGS2010

Geophysical Data Collected off the South Shore of Martha's Vineyard, Massachusetts

The U.S. Geological Survey Woods Hole Science Center conducted a nearshore geophysical survey offshore of the southern coast of Martha's Vineyard, in the vicinity of the Martha's Vineyard Coastal Observatory in 2007. This mapping program was part of a larger research effort supporting the Office of Naval Research Ripples Directed-Research Initiative studies at Martha's Vineyard Coastal Observatory designed to improve our understanding of coastal sediment-transport processes. The survey was conducted aboard the Megan T. Miller August 9-13, 2007. The study area covers 35 square kilometers from about 0.2 kilometers to 5 kilometers offshore of the south shore of Martha's Vineyard, and ranges in depth from ~6 to 24 meters. The geophysical mapping utilized the following suite of high-resolution instrumentation to map the surficial sediment distribution, bathymetry, and sub-surface geology: a dual-frequency 100/500 kilohertz sidescan-sonar system, 234 kilohertz interferometric sonar, and 500 hertz -12 kilohertz chirp subbottom profiler. These geophysical data will be used to provide initial conditions for wave and circulation modeling within the study area.

Open-File Report↗
USGS2010

The IGBP-DIS global 1 km land cover data set, DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.

International Journal of Remote Sensing↗
USGS2020

USGS “Did You Feel It?” — Science and lessons from twenty years of citizen science-based macroseismology

The U.S. Geological Survey (USGS) “Did You Feel It?” (DYFI) system is an automatic method for rapidly collecting macroseismic intensity data from Internet users’ shaking and damage reports and for generating intensity maps immediately following felt earthquakes. DYFI has been in operation for nearly two decades (1999-2019) in the United States, and for nearly 15 years globally. During that period the amount of data collected is astounding: Over 5 million individual DYFI intensity reports—spanning all magnitude and distance ranges—have been amassed and archived. DYFI allows for macroseismic data collection at rates and quantities never before imagined, and thus high-quality macroseismic intensity maps can be made almost immediately, and with more complete coverage at higher resolution than in the past. DYFI also allows for valuable positive interactions of the citizenry with a Federal science agency. In essence, the widespread adoption of DYFI—along with ShakeMap—has facilitated the general acceptance of the very concept of shaking intensity, fundamentally improving our agency’s ability to communicate both hazard and risk to the population. DYFI effectively confirms the importance of reporting and inculcating the public’s understanding of intensity—in addition to magnitude—for a proper perspective of earthquake risk-related decision-making. Furthermore, the vast amount of DYFI data allow for data-rich analyses of otherwise intractable seismological, sociological, and earthquake impact studies that range from quantifying the shaking due to induced earthquakes, to human response and risk perception, to relating recorded shaking metrics to macroseismic effects, and the attenuation of intensity with magnitude and distance. Naturally, web-based data collection also poses challenges. After two decades of experience acquiring data with the DYFI system, we address some of these hallenges by documenting refinements to our algorithmic and operational procedures that have evolved over that time. Lastly, we outline new opportune research and development directions for our DYFI approach to citizen seismology.

Frontiers in Earth Science↗
USGS2007

Rapid Data Delivery System (RDDS)

Since the start of the active 2000 summer fire season, the U. S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC) has been actively engaged in providing crucial and timely support to Federal, State, and local natural hazards monitoring, analysis, response, and recovery activities. As part of this support, RMGSC has developed the Rapid Data Delivery System (RDDS) to provide emergency and incident response teams with timely access to geospatial data. The RDDS meets these needs by combining a simple web-enabled data viewer for the selection and preview of vector and raster geospatial data with an easy to use data ordering form. The RDDS viewer also incorporates geospatial locations for current natural hazard incidents, including wildfires, earthquakes, hurricanes, and volcanoes, allowing incident responders to quickly focus on their area of interest for data selection.

Fact Sheet↗
USGS2012

Archive of post-Hurricane Charley coastal oblique aerial photographs collected during U.S. Geological Survey field activity 04CCH01 from Marco Island to Fort DeSoto, Florida, August 15, 2004

On August 15, 2004, the U.S. Geological Survey (USGS) conducted an oblique aerial photographic survey off the southwest coast of Florida, from Marco Island to Fort DeSoto, aboard a Navajo Chieftain airplane, tail number N2KK, at an altitude of 500 ft and approximately 1000 ft offshore. These photographs were used to document coastal changes such as beach erosion and overwash caused by Hurricane Charley. They will also be used as baseline data for future coastal change. The oblique photography also served as qualitative ground truthing for the Experimental Advanced Airborne Research Lidar (EAARL) coastal topography and bathymetry data collected on August 16, 2004 (Bonisteel and others, 2009). This report serves as an archive of photographs collected during the August 15, 2004, post-Hurricane Charley coastal oblique aerial survey along with associated flight path maps, KML files, navigation files, digital Field Activity Collection System (FACS) logs, and Federal Geographic Data Committee (FGDC) metadata. Refer to the Acronyms page for expansions of all acronyms and abbreviations used in this report. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 04CCH01 tells us the data were collected in 2004 for the Coastal Change Hazards (CCH) study and the data were collected during the first field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the ID number. Two separate records of flight navigation were collected during the survey. The first was a continuous ASCII text file from the PLGR that recorded only latitudes, longitudes, and altitudes every 30 sec for the entire flight. No time values were recorded by the PLGR. The second navigation record was recorded by a Trimble Centurion GPS and converted to subtitles on the video, using a Compix Titler unit. The video was shot continuously during the survey. The video subtitles recorded day, month, year, latitude, longitude, and time in hours, minutes, and seconds. In order to produce a digital record of the navigation values that included latitude, longitude, and time, each was manually extracted from the video every 5 min, and these values were matched to the latitude and longitude in the PLGR file. Next, the time was interpolated between these 5-min fixes using Excel to produce time values for each navigation fix recorded in the PLGR file. The location of each photograph taken was determined in the following manner. A Nikon MF-14 data back marks the time each photograph was acquired on the lower right corner of the image in day, hour, and minute format (in UTC). These values were entered from the photographs into an Excel spreadsheet. It is assumed for the purposes of locating the images that the photographs were taken at a constant rate during any given minute of flight. To assign the time value in seconds to each photograph, the number of photographs taken during each minute was evenly distributed across that minute. For example, if 15 photographs were taken during minute 19:00:00, we assume that a picture was taken every 4 sec. The photographs were assigned the time values 19:00:00, 19:00:04, 19:00:08, and so on. The video time navigation file was then merged with the new interpolated photograph file based on time to produce the point of which each photograph was collected. As a result, the positions assigned to each photograph are an estimate of the aircraft position, not the location of the landmark photographed. The photographs provided here are JPEG scanned images of the analog slides. The metadata values for photo creation time, GPS latitude, GPS longitude, GPS position, keywords, credit, artist, caption, copyright, and contact were added to each photograph's EXIF header using EXIFtools (see the Software page). Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet, or, when viewing the Google Earth KML file, by clicking on the marker and then clicking on either the thumbnail or the link below the thumbnail. The KML files were created using the photographic navigation files and the CreateKML JavaScript (see the Software page). To view the survey maps and navigation files, and for more information about these items, see the Navigation page. Figure 1 displays the acquisition geometry. The tables provide detailed information about the assigned location, name, data, and time the photograph was taken along with links to the photo and corresponding 5-min contact sheet. Refer to table 1 and table 2 for details of the northern and southern county photographs, respectively.

Florida↗