BioMon: A Google Earth Based Continuous Biomass Monitoring System (Demo Paper)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju

2009-01-01

We demonstrate a Google Earth based novel visualization system for continuous monitoring of biomass at regional and global scales. This system is integrated with a back-end spatiotemporal data mining system that continuously detects changes using high temporal resolution MODIS images. In addition to the visualization, we demonstrate novel query features of the system that provides insights into the current conditions of the landscape.

Efficiently Communicating Rich Heterogeneous Geospatial Data from the FeMO2008 Dive Cruise with FlashMap on EarthRef.org

NASA Astrophysics Data System (ADS)

Minnett, R. C.; Koppers, A. A.; Staudigel, D.; Staudigel, H.

2008-12-01

EarthRef.org is comprehensive and convenient resource for Earth Science reference data and models. It encompasses four main portals: the Geochemical Earth Reference Model (GERM), the Magnetics Information Consortium (MagIC), the Seamount Biogeosciences Network (SBN), and the Enduring Resources for Earth Science Education (ERESE). Their underlying databases are publically available and the scientific community has contributed widely and is urged to continue to do so. However, the net result is a vast and largely heterogeneous warehouse of geospatial data ranging from carefully prepared maps of seamounts to geochemical data/metadata, daily reports from seagoing expeditions, large volumes of raw and processed multibeam data, images of paleomagnetic sampling sites, etc. This presents a considerable obstacle for integrating other rich media content, such as videos, images, data files, cruise tracks, and interoperable database results, without overwhelming the web user. The four EarthRef.org portals clearly lend themselves to a more intuitive user interface and has, therefore, been an invaluable test bed for the design and implementation of FlashMap, a versatile KML-driven geospatial browser written for reliability and speed in Adobe Flash. FlashMap allows layers of content to be loaded and displayed over a streaming high-resolution map which can be zoomed and panned similarly to Google Maps and Google Earth. Many organizations, from National Geographic to the USGS, have begun using Google Earth software to display geospatial content. However, Google Earth, as a desktop application, does not integrate cleanly with existing websites requiring the user to navigate away from the browser and focus on a separate application and Google Maps, written in Java Script, does not scale up reliably to large datasets. FlashMap remedies these problems as a web-based application that allows for seamless integration of the real-time display power of Google Earth and the flexibility of the web without losing scalability and control of the base maps. Our Flash-based application is fully compatible with KML (Keyhole Markup Language) 2.2, the most recent iteration of KML, allowing users with existing Google Earth KML files to effortlessly display their geospatial content embedded in a web page. As a test case for FlashMap, the annual Iron-Oxidizing Microbial Observatory (FeMO) dive cruise to the Loihi Seamount, in conjunction with data available from ongoing and published FeMO laboratory studies, showcases the flexibility of this single web-based application. With a KML 2.2 compatible web-service providing the content, any database can display results in FlashMap. The user can then hide and show multiple layers of content, potentially from several data sources, and rapidly digest a vast quantity of information to narrow the search results. This flexibility gives experienced users the ability to drill down to exactly the record they are looking for (SERC at Carleton College's educational application of FlashMap at http://serc.carleton.edu/sp/erese/activities/22223.html) and allows users familiar with Google Earth the ability to load and view geospatial data content within a browser from any computer with an internet connection.

Integration of Geophysical and Geochemical Data

NASA Astrophysics Data System (ADS)

Yamagishi, Y.; Suzuki, K.; Tamura, H.; Nagao, H.; Yanaka, H.; Tsuboi, S.

2006-12-01

Integration of geochemical and geophysical data would give us a new insight to the nature of the Earth. It should advance our understanding for the dynamics of the Earth's interior and surface processes. Today various geochemical and geophysical data are available on Internet. These data are stored in various database systems. Each system is isolated and provides own format data. The goal of this study is to display both the geochemical and geophysical data obtained from such databases together visually. We adopt Google Earth as the presentation tool. Google Earth is virtual globe software and is provided free of charge by Google, Inc. Google Earth displays the Earth's surface using satellite images with mean resolution of ~15m. We display any graphical features on Google Earth by KML format file. We have developed softwares to convert geochemical and geophysical data to KML file. First of all, we tried to overlay data from Georoc and PetDB and seismic tomography data on Google Earth. Georoc and PetDB are both online database systems for geochemical data. The data format of Georoc is CSV and that of PetDB is Microsoft Excel. The format of tomography data we used is plain text. The conversion software can process these different file formats. The geochemical data (e. g. compositional abundance) is displayed as a three-dimensional column on the Earth's surface. The shape and color of the column mean the element type. The size and color tone vary according to the abundance of the element. The tomography data can be converted into a KML file for each depth. This overlay plot of geochemical data and tomography data should help us to correlate internal temperature anomalies to geochemical anomalies, which are observed at the surface of the Earth. Our tool can convert any geophysical and geochemical data to a KML as long as the data is associated with longitude and latitude. We are going to support more geophysical data formats. In addition, we are currently trying to obtain scientific insights for the Earth's interior based on the view of both geophysical and geochemical data on Google Earth.

Recent Advances in Geospatial Visualization with the New Google Earth

NASA Astrophysics Data System (ADS)

Anderson, J. C.; Poyart, E.; Yan, S.; Sargent, R.

2017-12-01

Google Earth's detailed, world-wide imagery and terrain data provide a rich backdrop for geospatial visualization at multiple scales, from global to local. The Keyhole Markup Language (KML) is an open standard that has been the primary way for users to author and share data visualizations in Google Earth. Despite its ease of use and flexibility for relatively small amounts of data, users can quickly run into difficulties and limitations working with large-scale or time-varying datasets using KML in Google Earth. Recognizing these challenges, we present our recent work toward extending Google Earth to be a more powerful data visualization platform. We describe a new KML extension to simplify the display of multi-resolution map tile pyramids - which can be created by analysis platforms like Google Earth Engine, or by a variety of other map tile production pipelines. We also describe how this implementation can pave the way to creating novel data visualizations by leveraging custom graphics shaders. Finally, we present our investigations into native support in Google Earth for data storage and transport formats that are well-suited for big raster and vector data visualization. Taken together, these capabilities make it easier to create and share new scientific data visualization experiences using Google Earth, and simplify the integration of Google Earth with existing map data products, services, and analysis pipelines.

Google Earth-Based Grand Tours of the World's Ocean Basins and Marine Sediments

NASA Astrophysics Data System (ADS)

St John, K. K.; De Paor, D. G.; Suranovic, B.; Robinson, C.; Firth, J. V.; Rand, C.

2016-12-01

The GEODE project has produced a collection of Google Earth-based marine geology teaching resources that offer grand tours of the world's ocean basins and marine sediments. We use a map of oceanic crustal ages from Müller et al (2008; doi:10.1029/2007GC001743), and a set of emergent COLLADA models of IODP drill core data as a basis for a Google Earth tour introducing students to the world's ocean basins. Most students are familiar with basic seafloor spreading patterns but teaching experience suggests that few students have an appreciation of the number of abandoned ocean basins on Earth. Students also lack a valid visualization of the west Pacific where the oldest crust forms an isolated triangular patch and the ocean floor becomes younger towards the subduction zones. Our tour links geographic locations to mechanical models of rifting, seafloor spreading, subduction, and transform faulting. Google Earth's built-in earthquake and volcano data are related to ocean floor patterns. Marine sediments are explored in a Google Earth tour that draws on exemplary IODP core samples of a range of sediment types (e.g., turbidites, diatom ooze). Information and links are used to connect location to sediment type. This tour compliments a physical core kit of core catcher sections that can be employed for classroom instruction (geode.net/marine-core-kit/). At a larger scale, we use data from IMLGS to explore the distribution of the marine sediments types in the modern global ocean. More than 2,500 sites are plotted with access to original data. Students are guided to compare modern "type sections" of primary marine sediment lithologies, as well as examine site transects to address questions of bathymetric setting, ocean circulation, chemistry (e.g., CCD), and bioproductivity as influences on modern seafloor sedimentation. KMZ files, student exercises, and tips for instructors are available at geode.net/exploring-marine-sediments-using-google-earth.

Google earth as a source of ancillary material in a history of psychology class.

PubMed

Stevison, Blake K; Biggs, Patrick T; Abramson, Charles I

2010-06-01

This article discusses the use of Google Earth to visit significant geographical locations associated with events in the history of psychology. The process of opening files, viewing content, adding placemarks, and saving customized virtual tours on Google Earth are explained. Suggestions for incorporating Google Earth into a history of psychology course are also described.

Enhancing Geographic and Digital Literacy with a Student-Generated Course Portfolio in Google Earth

ERIC Educational Resources Information Center

Guertin, Laura; Stubbs, Christopher; Millet, Christopher; Lee, Tsan-Kuang; Bodek, Matthew

2012-01-01

Google Earth can serve as a platform for students to construct a course ePortfolio. By having students construct their own placemarks in a customized Google Earth file, students document their learning in a geospatial context, learn an innovative use of Google Earth, and have the opportunity for creativity and flexibility with disseminating their…

A Java-based tool for creating KML files from GPS waypoints

NASA Astrophysics Data System (ADS)

Kinnicutt, P. G.; Rivard, C.; Rimer, S.

2008-12-01

Google Earth provides a free tool with powerful capabilities for visualizing geoscience images and data. Commercial software tools exist for doing sophisticated digitizing and spatial modeling , but for the purposes of presentation, visualization and overlaying aerial images with data Google Earth provides much of the functionality. Likewise, with current technologies in GPS (Global Positioning System) systems and with Google Earth Plus, it is possible to upload GPS waypoints, tracks and routes directly into Google Earth for visualization. However, older technology GPS units and even low-cost GPS units found today may lack the necessary communications interface to a computer (e.g. no Bluetooth, no WiFi, no USB, no Serial, etc.) or may have an incompatible interface, such as a Serial port but no USB adapter available. In such cases, any waypoints, tracks and routes saved in the GPS unit or recorded in a field notebook must be manually transferred to a computer for use in a GIS system or other program. This presentation describes a Java-based tool developed by the author which enables users to enter GPS coordinates in a user-friendly manner, then save these coordinates in a Keyhole MarkUp Language (KML) file format, for visualization in Google Earth. This tool either accepts user-interactive input or accepts input from a CSV (Comma Separated Value) file, which can be generated from any spreadsheet program. This tool accepts input in the form of lat/long or UTM (Universal Transverse Mercator) coordinates. This presentation describes this system's applicability through several small case studies. This free and lightweight tool simplifies the task of manually inputting GPS data into Google Earth for people working in the field without an automated mechanism for uploading the data; for instance, the user may not have internet connectivity or may not have the proper hardware or software. Since it is a Java application and not a web- based tool, it can be installed on one's field laptop and the GPS data can be manually entered without the need for internet connectivity. This tool provides a table view of the GPS data, but lacks a KML viewer to view the data overlain on top of an aerial view, as this viewer functionality is provided in Google Earth. The tool's primary contribution lies in its more convenient method for entering the GPS data manually when automated technologies are not available.

Visualizing Geographic Data in Google Earth for Education and Outreach

NASA Astrophysics Data System (ADS)

Martin, D. J.; Treves, R.

2008-12-01

Google Earth is an excellent tool to help students and the public visualize scientific data as with low technical skill scientific content can be shown in three dimensions against a background of remotely sensed imagery. It therefore has a variety of uses in university education and as a tool for public outreach. However, in both situations it is of limited value if it is only used to attract attention with flashy three dimensional animations. In this poster we shall illustrate several applications that represent what we believe is good educational practice. The first example shows how the combination of a floor map and a projection of Google Earth on a screen can be used to produce active learning. Students are asked to imagine where they would build a house on Big Island Hawaii in order to avoid volcanic hazards. In the second example Google Earth is used to illustrate evidence over a range of scales in a description of Lake Agassiz flood events which would be more difficult to comprehend in a traditional paper based format. In the final example a simple text manipulation application "TMapper" is used to change the color palette of a thematic map generated by the students in Google Earth to teach them about the use of color in map design.

Learning to Map the Earth and Planets using a Google Earth - based Multi-student Game

NASA Astrophysics Data System (ADS)

De Paor, D. G.; Wild, S. C.; Dordevic, M.

2011-12-01

We report on progress in developing an interactive geological and geophysical mapping game employing the Google Earth, Google Moon, and Goole Mars virtual globes. Working in groups of four, students represent themselves on the Google Earth surface by selecting an avatar. One of the group drives to each field stop in a model vehicle using game-like controls. When they arrive at a field stop and get out of their field vehicle, students can control their own avatars' movements independently and can communicate with one another by text message. They are geo-fenced and receive automatic messages if they wander off target. Individual movements are logged and stored in a MySQL database for later analysis. Students collaborate on mapping decisions and submit a report to their instructor through a Javascript interface to the Google Earth API. Unlike real mapping, students are not restricted by geographic access and can engage in comparative mapping on different planets. Using newly developed techniques, they can also explore and map the sub-surface down to the core-mantle boundary. Virtual specimens created with a 3D scanner, Gigapan images of outcrops, and COLLADA models of mantle structures such as subducted lithospheric slabs all contribute to an engaging learning experience.

Positional Accuracy Assessment of Googleearth in Riyadh

NASA Astrophysics Data System (ADS)

Farah, Ashraf; Algarni, Dafer

2014-06-01

Google Earth is a virtual globe, map and geographical information program that is controlled by Google corporation. It maps the Earth by the superimposition of images obtained from satellite imagery, aerial photography and GIS 3D globe. With millions of users all around the globe, GoogleEarth® has become the ultimate source of spatial data and information for private and public decision-support systems besides many types and forms of social interactions. Many users mostly in developing countries are also using it for surveying applications, the matter that raises questions about the positional accuracy of the Google Earth program. This research presents a small-scale assessment study of the positional accuracy of GoogleEarth® Imagery in Riyadh; capital of Kingdom of Saudi Arabia (KSA). The results show that the RMSE of the GoogleEarth imagery is 2.18 m and 1.51 m for the horizontal and height coordinates respectively.

Sally Ride EarthKAM - Automated Image Geo-Referencing Using Google Earth Web Plug-In

NASA Technical Reports Server (NTRS)

Andres, Paul M.; Lazar, Dennis K.; Thames, Robert Q.

2013-01-01

Sally Ride EarthKAM is an educational program funded by NASA that aims to provide the public the ability to picture Earth from the perspective of the International Space Station (ISS). A computer-controlled camera is mounted on the ISS in a nadir-pointing window; however, timing limitations in the system cause inaccurate positional metadata. Manually correcting images within an orbit allows the positional metadata to be improved using mathematical regressions. The manual correction process is time-consuming and thus, unfeasible for a large number of images. The standard Google Earth program allows for the importing of KML (keyhole markup language) files that previously were created. These KML file-based overlays could then be manually manipulated as image overlays, saved, and then uploaded to the project server where they are parsed and the metadata in the database is updated. The new interface eliminates the need to save, download, open, re-save, and upload the KML files. Everything is processed on the Web, and all manipulations go directly into the database. Administrators also have the control to discard any single correction that was made and validate a correction. This program streamlines a process that previously required several critical steps and was probably too complex for the average user to complete successfully. The new process is theoretically simple enough for members of the public to make use of and contribute to the success of the Sally Ride EarthKAM project. Using the Google Earth Web plug-in, EarthKAM images, and associated metadata, this software allows users to interactively manipulate an EarthKAM image overlay, and update and improve the associated metadata. The Web interface uses the Google Earth JavaScript API along with PHP-PostgreSQL to present the user the same interface capabilities without leaving the Web. The simpler graphical user interface will allow the public to participate directly and meaningfully with EarthKAM. The use of similar techniques is being investigated to place ground-based observations in a Google Mars environment, allowing the MSL (Mars Science Laboratory) Science Team a means to visualize the rover and its environment.

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Tiffany Montague, Technical Program Manager for NASA and Google Lunar X PRIZE, Google, Inc., speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

KML Tours: A New Platform for Exploring and Sharing Geospatial Data

NASA Astrophysics Data System (ADS)

Barcay, D. P.; Weiss-Malik, M.

2009-12-01

Google Earth and other virtual globes have allowed millions of people to explore the world from their own home. This technology has also raised the bar for professional visualizations: enabling interactive 3D visualizations to be created from massive data-sets, and shared using the KML language. For academics and professionals alike, an engaging presentation of your geospatial data is generally expected and can be the most effective form of advertisement. To that end, we released 'Touring' in Google Earth 5.0: a new medium for cinematic expression, visualized in Google Earth and written as extensions to the KML language. In a KML tour, the author has fine-grained control over the entire visual experience: precisely moving the virtual camera through the world while dynamically modifying the content, style, position, and visibility of the displayed data. An author can synchronize audio to this experience, bringing further immersion to a visualization. KML tours can help engage a broad user-base and conveying subtle concepts that aren't immediately apparent in traditional geospatial content. Unlike a pre-rendered video, a KML Tour maintains the rich interactivity of Google Earth, allowing users to continue exploring your content, and to mash-up other content with your visualization. This session will include conceptual explanations of the Touring feature in Google Earth, the structure of the touring KML extensions, as well as examples of compelling tours.

Google Earth as a method for connecting scientific research with the World

NASA Astrophysics Data System (ADS)

Graham, J. R.

2012-12-01

Google Earth has proven itself to be an exceptionally successful and ambitious application: fully capable as a scientific tool, yet able to also satisfy the intellectual and virtual touristic needs of students, educators and the general public. It is difficult to overstate Google Earth's impact on our understanding of the World we inhabit, and yet there is also considerable potential that remains unexplored. This paper will discuss Google Earth's potential as a social network for the science community - connecting the general public with scientists, and scientists with their research. This paper will look at the University of Lethbridge's RAVE (Reaching Audiences through Virtual Entryways) project as a model for how this social network can function within the Google Earth environment.

Visualizing Mars data and imagery with Google Earth

NASA Astrophysics Data System (ADS)

Beyer, R. A.; Broxton, M.; Gorelick, N.; Hancher, M.; Lundy, M.; Kolb, E.; Moratto, Z.; Nefian, A.; Scharff, T.; Weiss-Malik, M.

2009-12-01

There is a vast store of planetary geospatial data that has been collected by NASA but is difficult to access and visualize. Virtual globes have revolutionized the way we visualize and understand the Earth, but other planetary bodies including Mars and the Moon can be visualized in similar ways. Extraterrestrial virtual globes are poised to revolutionize planetary science, bring an exciting new dimension to science education, and allow ordinary users to explore imagery being sent back to Earth by planetary science satellites. The original Google Mars Web site allowed users to view base maps of Mars via the Web, but it did not have the full features of the 3D Google Earth client. We have previously demonstrated the use of Google Earth to display Mars imagery, but now with the launch of Mars in Google Earth, there is a base set of Mars data available for anyone to work from and add to. There are a variety of global maps to choose from and display. The Terrain layer has the MOLA gridded data topography, and where available, HRSC terrain models are mosaicked into the topography. In some locations there is also meter-scale terrain derived from HiRISE stereo imagery. There is rich information in the form of the IAU nomenclature database, data for the rovers and landers on the surface, and a Spacecraft Imagery layer which contains the image outlines for all HiRISE, CTX, CRISM, HRSC, and MOC image data released to the PDS and links back to their science data. There are also features like the Traveler's Guide to Mars, Historic Maps, Guided Tours, as well as the 'Live from Mars' feature, which shows the orbital tracks of both the Mars Odyssey and Mars Reconnaissance Orbiter for a few days in the recent past. It shows where they have acquired imagery, and also some preview image data. These capabilities have obvious public outreach and education benefits, but the potential benefits of allowing planetary scientists to rapidly explore these large and varied data collections—in geological context and within a single user interface—are also becoming evident. Because anyone can produce additional KML content for use in Google Earth, scientists can customize the environment to their needs as well as publish their own processed data and results for others to use. Many scientists and organizations have begun to do this already, resulting in a useful and growing collection of planetary-science-oriented Google Earth layers.

Towards a geospatial wikipedia

NASA Astrophysics Data System (ADS)

Fritz, S.; McCallum, I.; Schill, C.; Perger, C.; Kraxner, F.; Obersteiner, M.

2009-04-01

Based on the Google Earth (http://earth.google.com) platform we have developed a geospatial Wikipedia (geo-wiki.org). The tool allows everybody in the world to contribute to spatial validation and is made available to the internet community interested in that task. We illustrate how this tool can be used for different applications. In our first application we combine uncertainty hotspot information from three global land cover datasets (GLC, MODIS, GlobCover). With an ever increasing amount of high resolution images available on Google Earth, it is becoming increasingly possible to distinguish land cover features with a high degree of accuracy. We first direct the land cover validation community to certain hotspots of land cover uncertainty and then ask them to fill in a small popup menu on type of land cover, possibly a picture at that location with the different cardinal points as well as date and what type of validation was chosen (google earth imagery/panoramio or if the person has ground truth data). We have implemented the tool via a land cover validation community at FACEBOOK which is based on a snowball system which allows the tracking of individuals and the possibility to ignore users which misuse the system. In a second application we illustrate how the tool could possibly be used for mapping malaria occurrence and small water bodies as well as overall malaria risk. For this application we have implemented a polygon as well as attribute function using Google maps as along with virtual earth using openlayers. The third application deals with illegal logging and how an alert system for illegal logging detection within a certain land tenure system could be implemented. Here we show how the tool can be used to document illegal logging via a YouTube video.

A Google Earth Grand Tour of the Terrestrial Planets

ERIC Educational Resources Information Center

De Paor, Declan; Coba, Filis; Burgin, Stephen

2016-01-01

Google Earth is a powerful instructional resource for geoscience education. We have extended the virtual globe to include all terrestrial planets. Downloadable Keyhole Markup Language (KML) files (Google Earth's scripting language) associated with this paper include lessons about Mercury, Venus, the Moon, and Mars. We created "grand…

Teaching Waves with Google Earth

ERIC Educational Resources Information Center

Logiurato, Fabrizio

2012-01-01

Google Earth is a huge source of interesting illustrations of various natural phenomena. It can represent a valuable tool for science education, not only for teaching geography and geology, but also physics. Here we suggest that Google Earth can be used for introducing in an attractive way the physics of waves. (Contains 9 figures.)

Google Earth for Landowners: Insights from Hands-on Workshops

ERIC Educational Resources Information Center

Huff, Tristan

2014-01-01

Google Earth is an accessible, user-friendly GIS that can help landowners in their management planning. I offered hands-on Google Earth workshops to landowners to teach skills, including mapmaking, length and area measurement, and database management. Workshop participants were surveyed at least 6 months following workshop completion, and learning…

Web GIS in practice III: creating a simple interactive map of England's Strategic Health Authorities using Google Maps API, Google Earth KML, and MSN Virtual Earth Map Control

PubMed Central

Boulos, Maged N Kamel

2005-01-01

This eye-opener article aims at introducing the health GIS community to the emerging online consumer geoinformatics services from Google and Microsoft (MSN), and their potential utility in creating custom online interactive health maps. Using the programmable interfaces provided by Google and MSN, we created three interactive demonstrator maps of England's Strategic Health Authorities. These can be browsed online at – Google Maps API (Application Programming Interface) version, – Google Earth KML (Keyhole Markup Language) version, and – MSN Virtual Earth Map Control version. Google and MSN's worldwide distribution of "free" geospatial tools, imagery, and maps is to be commended as a significant step towards the ultimate "wikification" of maps and GIS. A discussion is provided of these emerging online mapping trends, their expected future implications and development directions, and associated individual privacy, national security and copyrights issues. Although ESRI have announced their planned response to Google (and MSN), it remains to be seen how their envisaged plans will materialize and compare to the offerings from Google and MSN, and also how Google and MSN mapping tools will further evolve in the near future. PMID:16176577

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Brian McLendon, VP of Engineering, Google, Inc., speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Alan Eustace, Senior VP of Engineering and Research, Google, Inc., speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

An overview of the web-based Google Earth coincident imaging tool

USGS Publications Warehouse

Chander, Gyanesh; Kilough, B.; Gowda, S.

2010-01-01

The Committee on Earth Observing Satellites (CEOS) Visualization Environment (COVE) tool is a browser-based application that leverages Google Earth web to display satellite sensor coverage areas. The analysis tool can also be used to identify near simultaneous surface observation locations for two or more satellites. The National Aeronautics and Space Administration (NASA) CEOS System Engineering Office (SEO) worked with the CEOS Working Group on Calibration and Validation (WGCV) to develop the COVE tool. The CEOS member organizations are currently operating and planning hundreds of Earth Observation (EO) satellites. Standard cross-comparison exercises between multiple sensors to compare near-simultaneous surface observations and to identify corresponding image pairs are time-consuming and labor-intensive. COVE is a suite of tools that have been developed to make such tasks easier.

Using Google Earth as an innovative tool for community mapping.

PubMed

Lefer, Theodore B; Anderson, Matthew R; Fornari, Alice; Lambert, Anastasia; Fletcher, Jason; Baquero, Maria

2008-01-01

Maps are used to track diseases and illustrate the social context of health problems. However, commercial mapping software requires special training. This article illustrates how nonspecialists used Google Earth, a free program, to create community maps. The Bronx, New York, is characterized by high levels of obesity and diabetes. Residents and medical students measured the variety and quality of food and exercise sources around a residency training clinic and a student-run free clinic, using Google Earth to create maps with minimal assistance. Locations were identified using street addresses or simply by pointing to them on a map. Maps can be shared via e-mail, viewed online with Google Earth or Google Maps, and the data can be incorporated into other mapping software.

Predicting the performance of local seismic networks using Matlab and Google Earth.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chael, Eric Paul

2009-11-01

We have used Matlab and Google Earth to construct a prototype application for modeling the performance of local seismic networks for monitoring small, contained explosions. Published equations based on refraction experiments provide estimates of peak ground velocities as a function of event distance and charge weight. Matlab routines implement these relations to calculate the amplitudes across a network of stations from sources distributed over a geographic grid. The amplitudes are then compared to ambient noise levels at the stations, and scaled to determine the smallest yield that could be detected at each source location by a specified minimum number ofmore » stations. We use Google Earth as the primary user interface, both for positioning the stations of a hypothetical local network, and for displaying the resulting detection threshold contours.« less

Presenting Big Data in Google Earth with KML

NASA Astrophysics Data System (ADS)

Hagemark, B.

2006-12-01

KML 2.1 and Google Earth 4 provides support to enable streaming of very large datasets, with "smart" loading of data at multiple levels of resolution and incremental update to previously loaded data. This presentation demonstrates this technology for use with the Google Earth KML geometry and image primitives and shows some techniques and tools for creating this KML.

Cultural Adventures for the Google[TM] Generation

ERIC Educational Resources Information Center

Dann, Tammy

2010-01-01

Google Earth is a computer program that allows users to view the Earth through satellite imagery and maps, to see cities from above and through street views, and to search for addresses and browse locations. Many famous buildings and structures from around the world have detailed 3D views accessible on Google Earth. It is possible to explore the…

Google Mercury: The Launch of a New Planet

NASA Astrophysics Data System (ADS)

Hirshon, B.; Chapman, C. R.; Edmonds, J.; Goldstein, J.; Hallau, K. G.; Solomon, S. C.; Vanhala, H.; Weir, H. M.; Messenger Education; Public Outreach Epo Team

2010-12-01

The NASA MESSENGER mission’s Education and Public Outreach (EPO) Team, in cooperation with Google, Inc., has launched Google Mercury, an immersive new environment on the Google Earth platform. Google Mercury features hundreds of surface features, most of them newly revealed by the three flybys of the innermost planet by the MESSENGER spacecraft. As with Google Earth, Google Mercury is available on line at no cost. This presentation will demonstrate how our team worked with Google staff, features we incorporated, how games can be developed within the Google Earth platform, and how others can add tours, games, and other educational features. Finally, we will detail new enhancements to be added once MESSENGER enters into orbit about Mercury in March 2011 and begins sending back compelling images and other global data sets on a daily basis. The MESSENGER EPO Team comprises individuals from the American Association for the Advancement of Science (AAAS); Carnegie Academy for Science Education (CASE); Center for Educational Resources (CERES) at Montana State University (MSU) - Bozeman; National Center for Earth and Space Science Education (NCESSE); Johns Hopkins University Applied Physics Laboratory (JHU/APL); National Air and Space Museum (NASM); Science Systems and Applications, Inc. (SSAI); and Southwest Research Institute (SwRI). Screen shot of Google Mercury as a work in progress

Earth Adventure: Virtual Globe-based Suborbital Atmospheric Greenhouse Gases Exploration

NASA Astrophysics Data System (ADS)

Wei, Y.; Landolt, K.; Boyer, A.; Santhana Vannan, S. K.; Wei, Z.; Wang, E.

2016-12-01

The Earth Venture Suborbital (EVS) mission is an important component of NASA's Earth System Science Pathfinder program that aims at making substantial advances in Earth system science through measurements from suborbital platforms and modeling researches. For example, the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project of EVS-1 collected measurements of greenhouse gases (GHG) on local to regional scales in the Alaskan Arctic. The Atmospheric Carbon and Transport - America (ACT-America) project of EVS-2 will provide advanced, high-resolution measurements of atmospheric profiles and horizontal gradients of CO2 and CH4.As the long-term archival center for CARVE and the future ACT-America data, the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) has been developing a versatile data management system for CARVE data to maximize their usability. One of these efforts is the virtual globe-based Suborbital Atmospheric GHG Exploration application. It leverages Google Earth to simulate the 185 flights flew by the C-23 Sherpa aircraft in 2012-2015 for the CARVE project. Based on Google Earth's 3D modeling capability and the precise coordinates, altitude, pitch, roll, and heading info of the aircraft recorded in every second during each flight, the application provides users accurate and vivid simulation of flight experiences, with an active 3D visualization of a C-23 Sherpa aircraft in view. This application provides dynamic visualization of GHG, including CO2, CO, H2O, and CH4 captured during the flights, at the same pace of the flight simulation in Google Earth. Photos taken during those flights are also properly displayed along the flight paths. In the future, this application will be extended to incorporate more complicated GHG measurements (e.g. vertical profiles) from the ACT-America project. This application leverages virtual globe technology to provide users an integrated framework to interactively explore information about GHG measurements and to link scientific measurements to the rich virtual planet environment provided by Google Earth. Positive feedbacks have been received from users. It provides a good example of extending basic data visualization into a knowledge discovery experience and maximizing the usability of Earth science observations.

Google earth mapping of damage from the Nigata-Ken-Chuetsu M6.6 earthquake of 16 July 2007

USGS Publications Warehouse

Kayen, Robert E.; Steele, WM. Clint; Collins, Brian; Walker, Kevin

2008-01-01

We describe the use of Google Earth during and after a large damaging earthquake thatstruck the central Japan coast on 16 July 2007 to collect and organize damage information and guide the reconnaissance activities. This software enabled greater real-time collaboration among scientists and engineers. After the field investigation, the Google Earth map is used as a final reporting product that was directly linked to the more traditional research report document. Finally, we analyze the use of the software within the context of a post-disaster reconnaissance investigation, and link it to student use of GoogleEarth in field situations

Landsat Imagery Enables Global Studies of Surface Trends

NASA Technical Reports Server (NTRS)

2015-01-01

Landsat 8 is the latest in the NASA-developed series of satellites that have provided a continuous picture of Earth for more than 40 years. Mountain View, California-based Google has incorporated Landsat data into several products, most recently generating a cloud-free view of Earth. Google has also teamed up with researchers at the University of Maryland and Goddard Space Flight Center to create a global survey showing changes in forest cover over many years-the first of its kind.

Utilization of Google Earth for Distribution Mapping of Cholangiocarcinoma: a Case Study in Satuek District, Buriram, Thailand.

PubMed

Rattanasing, Wannaporn; Kaewpitoon, Soraya J; Loyd, Ryan A; Rujirakul, Ratana; Yodkaw, Eakachai; Kaewpitoon, Natthawut

2015-01-01

Cholangiocarcinoma (CCA) is a serious public health problem in the Northeast of Thailand. CCA is considered to be an incurable and rapidly lethal disease. Knowledge of the distribution of CCA patients is necessary for management strategies. This study aimed to utilize the Geographic Information System and Google EarthTM for distribution mapping of cholangiocarcinoma in Satuek District, Buriram, Thailand, during a 5-year period (2008-2012). In this retrospective study data were collected and reviewed from the OPD cards, definitive cases of CCA were patients who were treated in Satuek hospital and were diagnosed with CCA or ICD-10 code C22.1. CCA cases were used to analyze and calculate with ArcGIS 9.2, all of data were imported into Google Earth using the online web page www.earthpoint.us. Data were displayed at village points. A total of 53 cases were diagnosed and identified as CCA. The incidence was 53.57 per 100,000 population (65.5 for males and 30.8 for females) and the majority of CCA cases were in stages IV and IIA. The average age was 67 years old. The highest attack rate was observed in Thung Wang sub-district (161.4 per 100,000 population). The map display at village points for CCA patients based on Google Earth gave a clear visual deistribution. CCA is still a major problem in Satuek district, Buriram province of Thailand. The Google Earth production process is very simple and easy to learn. It is suitable for the use in further development of CCA management strategies.

Engaging Middle School Students with Google Earth Technology to Analyze Ocean Cores as Evidence for Sea Floor Spreading

NASA Astrophysics Data System (ADS)

Prouhet, T.; Cook, J.

2006-12-01

Google Earth's ability to captivate students' attention, its ease of use, and its high quality images give it the potential to be an extremely effective tool for earth science educators. The unique properties of Google Earth satisfy a growing demand to incorporate technology in science instruction. Google Earth is free and relatively easy to use unlike some other visualization software. Students often have difficulty conceptualizing and visualizing earth systems, such as deep-ocean basins, because of the complexity and dynamic nature of the processes associated with them (e.g. plate tectonics). Google Earth's combination of aerial photography, satellite images and remote sensing data brings a sense of realism to science concepts. The unobstructed view of the ocean floor provided by this technology illustrates three-dimensional subsurface features such as rift valleys, subduction zones, and sea-mounts enabling students to better understand the seafloor's dynamic nature. Students will use Google Earth to navigate the sea floor, and examine Deep Sea Drilling Project (DSDP) core locations the from the Glomar Challenger Leg 3 expedition. The lesson to be implemented was expanded upon and derived from the Joint Oceanographic Insitute (JOI) Learning exercise, Nannofossils Reveal Seafloor Spreading. In addition, students take on the role of scientists as they graph and analyze paleontological data against the distance from the Mid Ocean Ridge. The integration of ocean core data in this three-dimensional view aids students' ability to draw and communicate valid conclusions about their scientific observations. A pre and post survey will be given to examine attitudes, self-efficacy, achievement and content mastery to a sample of approximately 300 eighth grade science students. The hypothesis is that the integration of Google Earth will significantly improve all areas of focus as mentioned above.

Results of new petrologic and remote sensing studies in the Big Bend region

NASA Astrophysics Data System (ADS)

Benker, Stevan Christian

The initial section of this manuscript involves the South Rim Formation, a series of 32.2-32 Ma comenditic quartz trachytic-rhyolitic volcanics and associated intrusives, erupted and was emplaced in Big Bend National Park, Texas. Magmatic parameters have only been interpreted for one of the two diverse petrogenetic suites comprising this formation. Here, new mineralogic data for the South Rim Formation rocks are presented. Magmatic parameters interpreted from these data assist in deciphering lithospheric characteristics during the mid-Tertiary. Results indicate low temperatures (< 750 °C), reduced conditions (generally below the FMQ buffer), and low pressures (≤ 100 MPa) associated with South Rim Formation magmatism with slight conditional differences between the two suites. Newly discovered fayalite microphenocrysts allowed determination of oxygen fugacity values (between -0.14 and -0.25 DeltaFMQ over temperature ranges of 680-700 °C), via mineral equilibria based QUILF95 calculations, for Emory Peak Suite. Petrologic information is correlated with structural evidence from Trans-Pecos Texas and adjacent regions to evaluate debated timing of tectonic transition (Laramide compression to Basin and Range extension) and onset of the southern Rio Grande Rift during the mid-Tertiary. The A-type and peralkaline characteristics of the South Rim Formation and other pre-31 Ma magmatism in Trans-Pecos Texas, in addition to evidence implying earlier Rio Grande Rift onset in Colorado and New Mexico, promotes a near-neutral to transtensional setting in Trans-Pecos Texas by 32 Ma. This idea sharply contrasts with interpretations of tectonic compression and arc-related magmatism until 31 Ma as suggested by some authors. However, evidence discussed cannot preclude a pre-36 Ma proposed by other authors. The later section of this manuscript involves research in the Big Bend area using Google Earth. At present there is high interest in using Google Earth in a variety of scientific investigations. However, program developers have disclosed limited information concerning the program and its accuracy. While some authors have attempted to independently constrain the accuracy of Google Earth, their results have potentially lost validity through time due to technological advances and updates to imagery archives. For this reason we attempt to constrain more current horizontal and vertical position accuracies for the Big Bend region of West Texas. In Google Earth a series of 268 data points were virtually traced along various early Tertiary unconformities in Big Bend National Park and Big Bend Ranch State Park. These data points were compared with high precision GPS measurements collected in field and yielded a horizontal position accuracy of 2.64 meters RMSE. Complications arose in determining vertical position accuracy for Google Earth because default keyhole markup language (.kml) files currently do not export elevation data. This drawback forces users to hand record and manually input elevation values listed on screen. This is a significant handicap rendering Google Earth data useless with larger datasets. However, in a workaround solution exempted elevation values can be replaced from other data sources based on Google Earth horizontal positioning. We used Fledermaus 3D three-dimensional visualization software to drape Google Earth horizontal positions over a National Elevation Dataset (NED) digital elevation map (DEM) in order to adopt a large set of elevation data. A vertical position accuracy of 1.63 meters RMSE was determined between 268 Google Earth data points and the NED. Since determined accuracies were considerably lower than those reported in previous investigations, we devoted a later portion of this investigation to testing Google Earth-NED data in paleo-surface modeling of the Big Bend region. An 18 x 30 kilometer area in easternmost Big Ranch State Park was selected to create a post-Laramide paleo-surface model via interpolation of approximately 2900 Google Earth-NED data points representing sections of an early Tertiary unconformity. The area proved difficult to model as unconformity tracing and interpolation were often hindered by surface inflation due to regional magmatism, burial of Laramide topography by subsequent volcanism and sedimentation, and overprinting of Basin & Range extensional features masking Laramide compressional features. Despite these difficulties, a model was created illustrating paleo-topographic highs in the southeastern Bofecillos Mountains and at Lajitas Mesa. Based on the amount of surface relief depicted, inconsistency with subsequent normal faulting, and distance from magmatic features capable of surface doming or inflation, we believe the paleo-topographic highs modeled legitimately reflect the post-Laramide surface. We interpret the paleo-surface in this area as reflecting a post-Laramide surface that has experienced significant erosion. We attribute the paleo-topographic highs as Laramide topography that was more resistant. The model also implies a southern paleo-drainage direction for the area and suggests the present day topographic low through which the Rio Grande flows may have formed very soon after the Laramide Orogeny. Based on the newly calculated horizontal and vertical position accuracies for the Big Bend region and results of modeled Google Earth-NED data in easternmost Big Bend Ranch State Park, it seems Google Earth can be effectively utilized for remote sensing and geologic studies, however we urge caution as developers remain reluctant to disclose detailed program information to the public.

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

NASA Deputy Administrator Lori Garver, speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

From Analysis to Impact: Challenges and Outcomes from Google's Cloud-based Platforms for Analyzing and Leveraging Petapixels of Geospatial Data

NASA Astrophysics Data System (ADS)

Thau, D.

2017-12-01

For the past seven years, Google has made petabytes of Earth observation data, and the tools to analyze it, freely available to researchers around the world via cloud computing. These data and tools were initially available via Google Earth Engine and are increasingly available on the Google Cloud Platform. We have introduced a number of APIs for both the analysis and presentation of geospatial data that have been successfully used to create impactful datasets and web applications, including studies of global surface water availability, global tree cover change, and crop yield estimation. Each of these projects used the cloud to analyze thousands to millions of Landsat scenes. The APIs support a range of publishing options, from outputting imagery and data for inclusion in papers, to providing tools for full scale web applications that provide analysis tools of their own. Over the course of developing these tools, we have learned a number of lessons about how to build a publicly available cloud platform for geospatial analysis, and about how the characteristics of an API can affect the kinds of impacts a platform can enable. This study will present an overview of how Google Earth Engine works and how Google's geospatial capabilities are extending to Google Cloud Platform. We will provide a number of case studies describing how these platforms, and the data they host, have been leveraged to build impactful decision support tools used by governments, researchers, and other institutions, and we will describe how the available APIs have shaped (or constrained) those tools. [Image Credit: Tyler A. Erickson

A Knowledge Portal and Collaboration Environment for the Earth Sciences

NASA Astrophysics Data System (ADS)

D'Agnese, F. A.

2008-12-01

Earth Knowledge is developing a web-based 'Knowledge Portal and Collaboration Environment' that will serve as the information-technology-based foundation of a modular Internet-based Earth-Systems Monitoring, Analysis, and Management Tool. This 'Knowledge Portal' is essentially a 'mash- up' of web-based and client-based tools and services that support on-line collaboration, community discussion, and broad public dissemination of earth and environmental science information in a wide-area distributed network. In contrast to specialized knowledge-management or geographic-information systems developed for long- term and incremental scientific analysis, this system will exploit familiar software tools using industry standard protocols, formats, and APIs to discover, process, fuse, and visualize existing environmental datasets using Google Earth and Google Maps. An early form of these tools and services is being used by Earth Knowledge to facilitate the investigations and conversations of scientists, resource managers, and citizen-stakeholders addressing water resource sustainability issues in the Great Basin region of the desert southwestern United States. These ongoing projects will serve as use cases for the further development of this information-technology infrastructure. This 'Knowledge Portal' will accelerate the deployment of Earth- system data and information into an operational knowledge management system that may be used by decision-makers concerned with stewardship of water resources in the American Desert Southwest.

Visualizing Cross-sectional Data in a Real-World Context

NASA Astrophysics Data System (ADS)

Van Noten, K.; Lecocq, T.

2016-12-01

If you could fly around your research results in three dimensions, wouldn't you like to do it? Visualizing research results properly during scientific presentations already does half the job of informing the public on the geographic framework of your research. Many scientists use the Google Earth™ mapping service (V7.1.2.2041) because it's a great interactive mapping tool for assigning geographic coordinates to individual data points, localizing a research area, and draping maps of results over Earth's surface for 3D visualization. However, visualizations of research results in vertical cross-sections are often not shown simultaneously with the maps in Google Earth. A few tutorials and programs to display cross-sectional data in Google Earth do exist, and the workflow is rather simple. By importing a cross-sectional figure into in the open software SketchUp Make [Trimble Navigation Limited, 2016], any spatial model can be exported to a vertical figure in Google Earth. In this presentation a clear workflow/tutorial is presented how to image cross-sections manually in Google Earth. No software skills, nor any programming codes are required. It is very easy to use, offers great possibilities for teaching and allows fast figure manipulation in Google Earth. The full workflow can be found in "Van Noten, K. 2016. Visualizing Cross-Sectional Data in a Real-World Context. EOS, Transactions AGU, 97, 16-19".The video tutorial can be found here: https://www.youtube.com/watch?v=Tr8LwFJ4RYU&Figure: Cross-sectional Research Examples Illustrated in Google Earth

USGS Coastal and Marine Geology Survey Data in Google Earth

NASA Astrophysics Data System (ADS)

Reiss, C.; Steele, C.; Ma, A.; Chin, J.

2006-12-01

The U.S. Geological Survey (USGS) Coastal and Marine Geology (CMG) program has a rich data catalog of geologic field activities and metadata called InfoBank, which has been a standard tool for researchers within and outside of the agency. Along with traditional web maps, the data are now accessible in Google Earth, which greatly expands the possible user audience. The Google Earth interface provides geographic orientation and panning/zooming capabilities to locate data relative to topography, bathymetry, and coastal areas. Viewing navigation with Google Earth's background imagery allows queries such as, why areas were not surveyed (answer presence of islands, shorelines, cliffs, etc.). Detailed box core subsample photos from selected sampling activities, published geotechnical data, and sample descriptions are now viewable on Google Earth, (for example, M-1-95-MB, P-2-95-MB, and P-1-97- MB box core samples). One example of the use of Google Earth is CMG's surveys of San Francisco's Ocean Beach since 2004. The surveys are conducted with an all-terrain vehicle (ATV) and shallow-water personal watercraft (PWC) equipped with Global Positioning System (GPS), and elevation and echo sounder data collectors. 3D topographic models with centimeter accuracy have been produced from these surveys to monitor beach and nearshore processes, including sand transport, sedimentation patterns, and seasonal trends. Using Google Earth, multiple track line data (examples: OB-1-05-CA and OB-2-05-CA) can be overlaid on beach imagery. The images also help explain the shape of track lines as objects are encountered.

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Andrew Chaikin, author of "A Man on the Moon" speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Buzz Aldrin, the second man to walk on the moon, speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Apollo 11 astronaut Buzz Aldrin, the second man to walk on the Moon, speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Miles O'Brien, former chief science and tech correspondent for CNN, speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

Google Moon Press Conference

NASA Image and Video Library

2009-07-19

Yoshinori Yoshimura, a respresentative from the Japan Aerospace Exploration Agency (JAXA), speaks during a press conference, Monday, July 20, 2009, announcing the launch of Moon in Google Earth, an immersive 3D atlas of the Moon, accessible within Google Earth 5.0, Monday, July 20, 2009, at the Newseum in Washington. Photo Credit: (NASA/Bill Ingalls)

State of the Oceans: A Satellite Data Processing System for Visualizing Near Real-Time Imagery on Google Earth

NASA Astrophysics Data System (ADS)

Thompson, C. K.; Bingham, A. W.; Hall, J. R.; Alarcon, C.; Plesea, L.; Henderson, M. L.; Levoe, S.

2011-12-01

The State of the Oceans (SOTO) web tool was developed at NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory (JPL) as an interactive means for users to visually explore and assess ocean-based geophysical parameters extracted from the latest archived data products. The SOTO system consists of four extensible modules, a data polling tool, a preparation and imaging package, image server software, and the graphical user interface. Together, these components support multi-resolution visualization of swath (Level 2) and gridded Level 3/4) data products as either raster- or vector- based KML layers on Google Earth. These layers are automatically updated periodically throughout the day. Current parameters available include sea surface temperature, chlorophyll concentration, ocean winds, sea surface height anomaly, and sea surface temperature anomaly. SOTO also supports mash-ups, allowing KML feeds from other sources to be overlaid directly onto Google Earth such as hurricane tracks and buoy data. A version of the SOTO software has also been installed at Goddard Space Flight Center (GSFC) to support the Land Atmosphere Near real-time Capability for EOS (LANCE). The State of the Earth (SOTE) has similar functionality to SOTO but supports different data sets, among them the MODIS 250m data product.

Jupyter meets Earth: Creating Comprehensible and Reproducible Scientific Workflows with Jupyter Notebooks and Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T.

2016-12-01

Deriving actionable information from Earth observation data obtained from sensors or models can be quite complicated, and sharing those insights with others in a form that they can understand, reproduce, and improve upon is equally difficult. Journal articles, even if digital, commonly present just a summary of an analysis that cannot be understood in depth or reproduced without major effort on the part of the reader. Here we show a method of improving scientific literacy by pairing a recently developed scientific presentation technology (Jupyter Notebooks) with a petabyte-scale platform for accessing and analyzing Earth observation and model data (Google Earth Engine). Jupyter Notebooks are interactive web documents that mix live code with annotations such as rich-text markup, equations, images, videos, hyperlinks and dynamic output. Notebooks were first introduced as part of the IPython project in 2011, and have since gained wide acceptance in the scientific programming community, initially among Python programmers but later by a wide range of scientific programming languages. While Jupyter Notebooks have been widely adopted for general data analysis, data visualization, and machine learning, to date there have been relatively few examples of using Jupyter Notebooks to analyze geospatial datasets. Google Earth Engine is cloud-based platform for analyzing geospatial data, such as satellite remote sensing imagery and/or Earth system model output. Through its Python API, Earth Engine makes petabytes of Earth observation data accessible, and provides hundreds of algorithmic building blocks that can be chained together to produce high-level algorithms and outputs in real-time. We anticipate that this technology pairing will facilitate a better way of creating, documenting, and sharing complex analyses that derive information on our Earth that can be used to promote broader understanding of the complex issues that it faces. http://jupyter.orghttps://earthengine.google.com

Visualizing Dynamic Weather and Ocean Data in Google Earth

NASA Astrophysics Data System (ADS)

Castello, C.; Giencke, P.

2008-12-01

Katrina. Climate change. Rising sea levels. Low lake levels. These headliners, and countless others like them, underscore the need to better understand our changing oceans and lakes. Over the past decade, efforts such as the Global Ocean Observing System (GOOS) have added to this understanding, through the creation of interoperable ocean observing systems. These systems, including buoy networks, gliders, UAV's, etc, have resulted in a dramatic increase in the amount of Earth observation data available to the public. Unfortunately, these data tend to be restrictive to mass consumption, owing to large file sizes, incompatible formats, and/or a dearth of user friendly visualization software. Google Earth offers a flexible way to visualize Earth observation data. Marrying high resolution orthoimagery, user friendly query and navigation tools, and the power of OGC's KML standard, Google Earth can make observation data universally understandable and accessible. This presentation will feature examples of meteorological and oceanographic data visualized using KML and Google Earth, along with tools and tips for integrating other such environmental datasets.

Making a report of a short trip in an ophiolitic complex with Google Earth

NASA Astrophysics Data System (ADS)

Aubret, Marianne

2017-04-01

Plate tectonics is taught in French secondary school (lower and upper-sixth). According to the curriculum, the comprehension of plate-tectonic processes and concepts should be based on field data. For example, the Alpine's ocean history is studied to understand how mountain ranges are formed. In this context, Corsica is a great open-air laboratory, but unfortunately, the traffic conditions are very difficult in the island and despite the short distances, it's almost impossible for teachers to take their students to the remarkable geologic spots. The «défilé de l'Inzecca» is one of them: there you can see a part of the alpine's ophiolitic complex. The aim of this activity is to elaborate a « KMZ folder » in Google Earth as a report of a short trip thanks to the students' data field; it is also the occasion to enrich the Google Earth KMZ folder already available for our teaching.

Conceptual Learning Outcomes of Virtual Experiential Learning: Results of Google Earth Exploration in Introductory Geoscience Courses

NASA Astrophysics Data System (ADS)

Bitting, Kelsey S.; McCartney, Marsha J.; Denning, Kathy R.; Roberts, Jennifer A.

2018-06-01

Virtual globe programs such as Google Earth replicate real-world experiential learning of spatial and geographic concepts by allowing students to navigate across our planet without ever leaving campus. However, empirical evidence for the learning value of these technological tools and the experience students gain by exploration assignments framed within them remains to be quantified and compared by student demographics. This study examines the impact of a Google Earth-based exploration assignment on conceptual understanding in introductory geoscience courses at a research university in the US Midwest using predominantly traditional college-age students from a range of majors. Using repeated-measures ANOVA and paired-samples t tests, we test the significance of the activity using pretest and posttest scores on a subset of items from the Geoscience Concept Inventory, and the interactive effects of student gender and ethnicity on student score improvement. Analyses show that learning from the Google Earth exploration activity is highly significant overall and for all but one of the concept inventory items. Furthermore, we find no significant interactive effects of class format, student gender, or student ethnicity on the magnitude of the score increases. These results provide strong support for the use of experiential learning in virtual globe environments for students in introductory geoscience and perhaps other disciplines for which direct observation of our planet's surface is conceptually relevant.

Leveraging Earth and Planetary Datasets to Support Student Investigations in an Introductory Geoscience Course

NASA Astrophysics Data System (ADS)

Ryan, Jeffrey; De Paor, Declan

2016-04-01

Engaging undergraduates in discovery-based research during their first two years of college was a listed priority in the 2012 Report of the USA President's Council of Advisors on Science and Technology (PCAST), and has been the focus of events and publications sponsored by the National Academies (NAS, 2015). Challenges faced in moving undergraduate courses and curricula in this direction are the paired questions of how to effectively provide such experiences to large numbers of students, and how to do so in ways that are cost- and time-effiicient for institutions and instructional faculty. In the geosciences, free access to of a growing number of global earth and planetary data resources and associated visualization tools permits one to build into introductory-level courses straightforward data interrogation and analysis activities that provide students with valuable experiences with the compilation and critical investigation of earth and planetary data. Google Earth provides global Earth and planetary imagery databases that span large ranges in resolution and in time, permitting easy examination of earth surface features and surface features on Mars or the Moon. As well, "community" data sources (i.e., Gigapan photographic collections and 3D visualizations of geologic features, as are supported by the NSF GEODE project) allow for intensive interrogation of specific geologic phenomena. Google Earth Engine provides access to rich satellite-based earth observation data, supporting studies of weather and related student efforts. GeoMapApp, the freely available visualization tool of the Interdisciplinary Earth Data Alliance (IEDA), permits examination of the seafloor and the integration of a range of third-party data. The "Earth" meteorological website (earth.nullschool.net) provides near real-time visualization of global weather and oceanic conditions, which in combination with weather option data from Google Earth permits a deeper interrogation of atmospheric conditions. In combination, these freely accessible data resources permit one to transform general- audience geoscience courses into extended investigations, in which students discover key information about the workings of our planet.

Google's Geo Education Outreach: Results and Discussion of Outreach Trip to Alaskan High Schools.

NASA Astrophysics Data System (ADS)

Kolb, E. J.; Bailey, J.; Bishop, A.; Cain, J.; Goddard, M.; Hurowitz, K.; Kennedy, K.; Ornduff, T.; Sfraga, M.; Wernecke, J.

2008-12-01

The focus of Google's Geo Education outreach efforts (http://www.google.com/educators/geo.html) is on helping primary, secondary, and post-secondary educators incorporate Google Earth and Sky, Google Maps, and SketchUp into their classroom lessons. In partnership with the University of Alaska, our Geo Education team members visited several remote Alaskan high schools during a one-week period in September. At each school, we led several 40-minute hands-on learning sessions in which Google products were used by the students to investigate local geologic and environmental processes. For the teachers, we provided several resources including follow-on lesson plans, example KML-based lessons, useful URL's, and website resources that multiple users can contribute to. This talk will highlight results of the trip and discuss how educators can access and use Google's Geo Education resources.

Google-Earth Based Visualizations for Environmental Flows and Pollutant Dispersion in Urban Areas

PubMed Central

Liu, Daoming; Kenjeres, Sasa

2017-01-01

In the present study, we address the development and application of an efficient tool for conversion of results obtained by an integrated computational fluid dynamics (CFD) and computational reaction dynamics (CRD) approach and their visualization in the Google Earth. We focus on results typical for environmental fluid mechanics studies at a city scale that include characteristic wind flow patterns and dispersion of reactive scalars. This is achieved by developing a code based on the Java language, which converts the typical four-dimensional structure (spatial and temporal dependency) of data results in the Keyhole Markup Language (KML) format. The visualization techniques most often used are revisited and implemented into the conversion tool. The potential of the tool is demonstrated in a case study of smog formation due to an intense traffic emission in Rotterdam (The Netherlands). It is shown that the Google Earth can provide a computationally efficient and user-friendly means of data representation. This feature can be very useful for visualization of pollution at street levels, which is of great importance for the city residents. Various meteorological and traffic emissions can be easily visualized and analyzed, providing a powerful, user-friendly tool for traffic regulations and urban climate adaptations. PMID:28257078

Information Technology Infusion Case Study: Integrating Google Earth(Trademark) into the A-Train Data Depot

NASA Technical Reports Server (NTRS)

Smith, Peter; Kempler, Steven; Leptoukh, Gregory; Chen, Aijun

2010-01-01

This poster paper represents the NASA funded project that was to employ the latest three dimensional visualization technology to explore and provide direct data access to heterogeneous A-Train datasets. Google Earth (tm) provides foundation for organizing, visualizing, publishing and synergizing Earth science data .

Google Earth in the middle school geography classroom: Its impact on spatial literacy and place geography understanding of students

NASA Astrophysics Data System (ADS)

Westgard, Kerri S. W.

Success in today's globalized, multi-dimensional, and connected world requires individuals to have a variety of skill sets -- i.e. oracy, numeracy, literacy, as well as the ability to think spatially. Student's spatial literacy, based on various national and international assessment results, indicates that even though there have been gains in U.S. scores over the past decade, overall performance, including those specific to spatial skills, are still below proficiency. Existing studies focused on the potential of virtual learning environment technology to reach students in a variety of academic areas, but a need still exists to study specifically the phenomenon of using Google Earth as a potentially more useful pedagogical tool to develop spatial literacy than the currently employed methods. The purpose of this study was to determine the extent to which graphicacy achievement scores of students who were immersed in a Google Earth environment were different from students who were provided with only two-dimensional instruction for developing spatial skills. Situated learning theory and the work of Piaget and Inhelder's Child's Conception of Space provided the theoretical grounding from which this study evolved. The National Research Council's call to develop spatial literacy, as seen in Learning to Think Spatially , provided the impetus to begin research. The target population (N = 84) for this study consisted of eighth grade geography students at an upper Midwest Jr. High School during the 2009-2010 academic year. Students were assigned to the control or experimental group based on when they had geography class. Control group students ( n = 44) used two-dimensional PowerPoint images to complete activities, while experimental group students (n = 40) were immersed in the three-dimensional Google Earth world for activity completion. Research data was then compiled and statistically analyzed to answer five research questions developed for this study. One-way ANOVAs were run on data collected and no statistically significant difference was found between the control and experimental group. However, two of the five research questions yielded practically significant data that indicates students who used Google Earth outperformed their counterparts who used PowerPoint on pattern prediction and spatial relationship understanding.

Interacting with Petabytes of Earth Science Data using Jupyter Notebooks, IPython Widgets and Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Granger, B.; Grout, J.; Corlay, S.

2017-12-01

The volume of Earth science data gathered from satellites, aircraft, drones, and field instruments continues to increase. For many scientific questions in the Earth sciences, managing this large volume of data is a barrier to progress, as it is difficult to explore and analyze large volumes of data using the traditional paradigm of downloading datasets to a local computer for analysis. Furthermore, methods for communicating Earth science algorithms that operate on large datasets in an easily understandable and reproducible way are needed. Here we describe a system for developing, interacting, and sharing well-documented Earth Science algorithms that combines existing software components: Jupyter Notebook: An open-source, web-based environment that supports documents that combine code and computational results with text narrative, mathematics, images, and other media. These notebooks provide an environment for interactive exploration of data and development of well documented algorithms. Jupyter Widgets / ipyleaflet: An architecture for creating interactive user interface controls (such as sliders, text boxes, etc.) in Jupyter Notebooks that communicate with Python code. This architecture includes a default set of UI controls (sliders, dropboxes, etc.) as well as APIs for building custom UI controls. The ipyleaflet project is one example that offers a custom interactive map control that allows a user to display and manipulate geographic data within the Jupyter Notebook. Google Earth Engine: A cloud-based geospatial analysis platform that provides access to petabytes of Earth science data via a Python API. The combination of Jupyter Notebooks, Jupyter Widgets, ipyleaflet, and Google Earth Engine makes it possible to explore and analyze massive Earth science datasets via a web browser, in an environment suitable for interactive exploration, teaching, and sharing. Using these environments can make Earth science analyses easier to understand and reproducible, which may increase the rate of scientific discoveries and the transition of discoveries into real-world impacts.

A Web-based Google-Earth Coincident Imaging Tool for Satellite Calibration and Validation

NASA Astrophysics Data System (ADS)

Killough, B. D.; Chander, G.; Gowda, S.

2009-12-01

The Group on Earth Observations (GEO) is coordinating international efforts to build a Global Earth Observation System of Systems (GEOSS) to meet the needs of its nine “Societal Benefit Areas”, of which the most demanding, in terms of accuracy, is climate. To accomplish this vision, satellite on-orbit and ground-based data calibration and validation (Cal/Val) of Earth observation measurements are critical to our scientific understanding of the Earth system. Existing tools supporting space mission Cal/Val are often developed for specific campaigns or events with little desire for broad application. This paper describes a web-based Google-Earth based tool for the calculation of coincident satellite observations with the intention to support a diverse international group of satellite missions to improve data continuity, interoperability and data fusion. The Committee on Earth Observing Satellites (CEOS), which includes 28 space agencies and 20 other national and international organizations, are currently operating and planning over 240 Earth observation satellites in the next 15 years. The technology described here will better enable the use of multiple sensors to promote increased coordination toward a GEOSS. The CEOS Systems Engineering Office (SEO) and the Working Group on Calibration and Validation (WGCV) support the development of the CEOS Visualization Environment (COVE) tool to enhance international coordination of data exchange, mission planning and Cal/Val events. The objective is to develop a simple and intuitive application tool that leverages the capabilities of Google-Earth web to display satellite sensor coverage areas and for the identification of coincident scene locations along with dynamic menus for flexibility and content display. Key features and capabilities include user-defined evaluation periods (start and end dates) and regions of interest (rectangular areas) and multi-user collaboration. Users can select two or more CEOS missions from a database including Satellite Tool Kit (STK) generated orbit information and perform rapid calculations to identify coincident scenes where the groundtracks of the CEOS mission instrument fields-of-view intersect. Calculated results are displayed on a customized Google-Earth web interface to view location and time information along with optional output to EXCEL table format. In addition, multiple viewports can be used for comparisons. COVE was first introduced to the CEOS WGCV community in May 2009. Since that time, the development of a prototype version has progressed. It is anticipated that the capabilities and applications of COVE can support a variety of international Cal/Val activities as well as provide general information on Earth observation coverage for education and societal benefit. This project demonstrates the utility of a systems engineering tool with broad international appeal for enhanced communication and data evaluation opportunities among international CEOS agencies. The COVE tool is publicly accessible via NASA servers.

Getting Your GIS Data into Google Earth: Data Conversion Tools and Tips

NASA Astrophysics Data System (ADS)

Nurik, R.; Marks, M.

2009-12-01

Google Earth is a powerful platform for displaying your data. You can easily visualize content using the Keyhole Markup Language (KML). But what if you don't have your data in KML format? GIS data comes in a wide variety of formats, including .shp files, CSV, and many others. What can you do? This session will walk you through some of the tools for converting data to KML format. We will explore a variety of tools, including: Google Earth Pro, GDAL/OGR, KML2KML, etc. This session will be paced so that you can follow along on your laptop if you wish. Should you want to follow along, bring a laptop, and install the trial versions of Google Earth Pro and KML2KML. It is also recommended that you download GDAL from gdal.org and install it on your system.

Next-generation Digital Earth.

PubMed

Goodchild, Michael F; Guo, Huadong; Annoni, Alessandro; Bian, Ling; de Bie, Kees; Campbell, Frederick; Craglia, Max; Ehlers, Manfred; van Genderen, John; Jackson, Davina; Lewis, Anthony J; Pesaresi, Martino; Remetey-Fülöpp, Gábor; Simpson, Richard; Skidmore, Andrew; Wang, Changlin; Woodgate, Peter

2012-07-10

A speech of then-Vice President Al Gore in 1998 created a vision for a Digital Earth, and played a role in stimulating the development of a first generation of virtual globes, typified by Google Earth, that achieved many but not all the elements of this vision. The technical achievements of Google Earth, and the functionality of this first generation of virtual globes, are reviewed against the Gore vision. Meanwhile, developments in technology continue, the era of "big data" has arrived, the general public is more and more engaged with technology through citizen science and crowd-sourcing, and advances have been made in our scientific understanding of the Earth system. However, although Google Earth stimulated progress in communicating the results of science, there continue to be substantial barriers in the public's access to science. All these factors prompt a reexamination of the initial vision of Digital Earth, and a discussion of the major elements that should be part of a next generation.

Visualizing Moon Data and Imagery with Google Earth

NASA Astrophysics Data System (ADS)

Weiss-Malik, M.; Scharff, T.; Nefian, A.; Moratto, Z.; Kolb, E.; Lundy, M.; Hancher, M.; Gorelick, N.; Broxton, M.; Beyer, R. A.

2009-12-01

There is a vast store of planetary geospatial data that has been collected by NASA but is difficult to access and visualize. Virtual globes have revolutionized the way we visualize and understand the Earth, but other planetary bodies including Mars and the Moon can be visualized in similar ways. Extraterrestrial virtual globes are poised to revolutionize planetary science, bring an exciting new dimension to science education, and allow ordinary users to explore imagery being sent back to Earth by planetary science satellites. The original Google Moon Web site was a limited series of maps and Apollo content. The new Moon in Google Earth feature provides a similar virtual planet experience for the Moon as we have for the Earth and Mars. We incorporated existing Clementine and Lunar Orbiter imagery for the basemaps and a combination of Kaguya LALT topography and some terrain created from Apollo Metric and Panoramic images. We also have information about the Apollo landings and other robotic landers on the surface, as well as historic maps and charts, and guided tours. Some of the first-released LROC imagery of the Apollo landing sites has been put in place, and we look forward to incorporating more data as it is released from LRO, Chandraayan-1, and Kaguya. These capabilities have obvious public outreach and education benefits, but the potential benefits of allowing planetary scientists to rapidly explore these large and varied data collections — in geological context and within a single user interface — are also becoming evident. Because anyone can produce additional KML content for use in Google Earth, scientists can customize the environment to their needs as well as publish their own processed data and results for others to use. Many scientists and organizations have begun to do this already, resulting in a useful and growing collection of planetary-science-oriented Google Earth layers. Screen shot of Moon in Google Earth, a freely downloadable application for visualizing Moon imagery and data.

Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation

PubMed Central

Moxley, Jerry H.; Bogomolni, Andrea; Hammill, Mike O.; Moore, Kathleen M. T.; Polito, Michael J.; Sette, Lisa; Sharp, W. Brian; Waring, Gordon T.; Gilbert, James R.; Halpin, Patrick N.; Johnston, David W.

2017-01-01

Abstract As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances. PMID:29599542

Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation.

PubMed

Moxley, Jerry H; Bogomolni, Andrea; Hammill, Mike O; Moore, Kathleen M T; Polito, Michael J; Sette, Lisa; Sharp, W Brian; Waring, Gordon T; Gilbert, James R; Halpin, Patrick N; Johnston, David W

2017-08-01

As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances.

Google Earth locations of USA and seafloor hydrothermal vents with associated rare earth element data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrew Fowler

Google Earth .kmz files that contain the locations of geothermal wells and thermal springs in the USA, and seafloor hydrothermal vents that have associated rare earth element data. The file does not contain the actual data, the actual data is available through the GDR website in two tier 3 data sets entitled "Compilation of Rare Earth Element Analyses from US Geothermal Fields and Mid Ocean Ridge (MOR) Hydrothermal Vents" and "Rare earth element content of thermal fluids from Surprise Valley, California"

Google Earth Mapping Exercises for Structural Geology Students--A Promising Intervention for Improving Penetrative Visualization Ability

ERIC Educational Resources Information Center

Giorgis, Scott

2015-01-01

Three-dimensional thinking skills are extremely useful for geoscientists, and at the undergraduate level, these skills are often emphasized in structural geology courses. Google Earth is a powerful tool for visualizing the three-dimensional nature of data collected on the surface of Earth. The results of a 5 y pre- and posttest study of the…

Near real-time qualitative monitoring of lake water chlorophyll globally using GoogleEarth Engine

NASA Astrophysics Data System (ADS)

Zlinszky, András; Supan, Peter; Koma, Zsófia

2017-04-01

Monitoring ocean chlorophyll and suspended sediment has been made possible using optical satellite imaging, and has contributed immensely to our understanding of the Earth and its climate. However, lake water quality monitoring has limitations due to the optical complexity of shallow, sediment- and organic matter-laden waters. Meanwhile, timely and detailed information on basic lake water quality parameters would be essential for sustainable management of inland waters. Satellite-based remote sensing can deliver area-covering, high resolution maps of basic lake water quality parameters, but scientific application of these datasets for lake monitoring has been hindered by limitations to calibration and accuracy evaluation, and therefore access to such data has been the privilege of scientific users. Nevertheless, since for many inland waters satellite imaging is the only source of monitoring data, we believe it is urgent to make map products of chlorophyll and suspended sediment concentrations available to a wide range of users. Even if absolute accuracy can not be validated, patterns, processes and qualitative information delivered by such datasets in near-real time can act as an early warning system, raise awareness to water quality processes and serve education, in addition to complementing local monitoring activities. By making these datasets openly available on the internet through an easy to use framework, dialogue between stakeholders, management and governance authorities can be facilitated. We use GoogleEarthEngine to access and process archive and current satellite data. GoogleEarth Engine is a development and visualization framework that provides access to satellite datasets and processing capacity for analysis at the Petabyte scale. Based on earlier investigations, we chose the fluorescence line height index to represent water chlorophyll concentration. This index relies on the chlorophyll fluorescence peak at 680 nm, and has been tested for open ocean but also inland lake situations for MODIS and MERIS satellite sensor data. In addition to being relatively robust and less sensitive to atmospheric influence, this algorithm is also very simple, being based on the height of the 680 nm peak above the linear interpolation of the two neighbouring bands. However, not all satellite datasets suitable for FLH are catalogued for GoogleEarth Engine. In the current testing phase, Landsat 7, Landsat 8 (30 m resolution), and Sentinel 2 (20 m) are being tested. Landsat 7 has suitable band configuration, but has a strip error due to a sensor problem. Landsat 8 and Sentinel 2 lack a single spectral optimal for FLH. Sentinel 3 would be an optimal data source and has shown good performace during small-scale initial tests, but is not distributed globally for GoogleEarth Engine. In addition to FLH data from these satellites, our system delivers cloud and ice masking, qualitative suspended sediment data (based on the band closest to 600 nm) and true colour images, all within an easy-to-use Google Maps background. This allows on-demand understanding and interpretation of water quality patterns and processes in near real time. While the system is still under development, we believe it could significantly contribute to lake water quality management and monitoring worldwide.

Do Interactive Globes and Games Help Students Learn Planetary Science?

NASA Astrophysics Data System (ADS)

Coba, Filis; Burgin, Stephen; De Paor, Declan; Georgen, Jennifer

2016-01-01

The popularity of animations and interactive visualizations in undergraduate science education might lead one to assume that these teaching aids enhance student learning. We tested this assumption for the case of the Google Earth virtual globe with a comparison of control and treatment student groups in a general education class of over 370 students at a large public university. Earth and Planetary Science course content was developed in two formats: using Keyhole Markup Language (KML) to create interactive tours in Google Earth (the treatment group) and Portable Document Format (PDF) for on-screen reading (the control group). The PDF documents contained identical text and images to the placemark balloons or "tour stops" in the Google Earth version. Some significant differences were noted between the two groups based on the immediate post-questionnaire with the KML students out-performing the PDF students, but not on the delayed measure. In a separate but related project, we undertake preliminary investigations into methods of teaching basic concepts in planetary mantle convection using numerical simulations. The goal of this project is to develop an interface with a two-dimensional finite element model that will allow students to vary parameters such as the temperatures assigned to the boundaries of the model domain, to help them actively explore important variables that control convection.

Cartographic analyses of geographic information available on Google Earth Images

NASA Astrophysics Data System (ADS)

Oliveira, J. C.; Ramos, J. R.; Epiphanio, J. C.

2011-12-01

The propose was to evaluate planimetric accuracy of satellite images available on database of Google Earth. These images are referents to the vicinities of the Federal Univertisity of Viçosa, Minas Gerais - Brazil. The methodology developed evaluated the geographical information of three groups of images which were in accordance to the level of detail presented in the screen images (zoom). These groups of images were labeled to Zoom 1000 (a single image for the entire study area), Zoom 100 (formed by a mosaic of 73 images) and Zoom 100 with geometric correction (this mosaic is like before, however, it was applied a geometric correction through control points). In each group of image was measured the Cartographic Accuracy based on statistical analyses and brazilian's law parameters about planimetric mapping. For this evaluation were identified 22 points in each group of image, where the coordinates of each point were compared to the coordinates of the field obtained by GPS (Global Positioning System). The Table 1 show results related to accuracy (based on a threshold equal to 0.5 mm * mapping scale) and tendency (abscissa and ordinate) between the coordinates of the image and the coordinates of field. Table 1 The geometric correction applied to the Group Zoom 100 reduced the trends identified earlier, and the statistical tests pointed a usefulness of the data for a mapping at a scale of 1/5000 with error minor than 0.5 mm * scale. The analyses proved the quality of cartographic data provided by Google, as well as the possibility of reduce the divergences of positioning present on the data. It can be concluded that it is possible to obtain geographic information database available on Google Earth, however, the level of detail (zoom) used at the time of viewing and capturing information on the screen influences the quality cartographic of the mapping. Although cartographic and thematic potential present in the database, it is important to note that both the software as data distributed by Google Earth has policies for use and distribution.
Table 1 - PLANIMETRIC ANALYSIS

Next-generation Digital Earth

PubMed Central

Goodchild, Michael F.; Guo, Huadong; Annoni, Alessandro; Bian, Ling; de Bie, Kees; Campbell, Frederick; Craglia, Max; Ehlers, Manfred; van Genderen, John; Jackson, Davina; Lewis, Anthony J.; Pesaresi, Martino; Remetey-Fülöpp, Gábor; Simpson, Richard; Skidmore, Andrew; Wang, Changlin; Woodgate, Peter

2012-01-01

A speech of then-Vice President Al Gore in 1998 created a vision for a Digital Earth, and played a role in stimulating the development of a first generation of virtual globes, typified by Google Earth, that achieved many but not all the elements of this vision. The technical achievements of Google Earth, and the functionality of this first generation of virtual globes, are reviewed against the Gore vision. Meanwhile, developments in technology continue, the era of “big data” has arrived, the general public is more and more engaged with technology through citizen science and crowd-sourcing, and advances have been made in our scientific understanding of the Earth system. However, although Google Earth stimulated progress in communicating the results of science, there continue to be substantial barriers in the public’s access to science. All these factors prompt a reexamination of the initial vision of Digital Earth, and a discussion of the major elements that should be part of a next generation. PMID:22723346

Google Earth as a (Not Just) Geography Education Tool

ERIC Educational Resources Information Center

Patterson, Todd C.

2007-01-01

The implementation of Geographic Information Science (GIScience) applications and discussion of GIScience-related themes are useful for teaching fundamental geographic and technological concepts. As one of the newest geographic information tools available on the World Wide Web, Google Earth has considerable potential to enhance methods for…

A Land-Use-Planning Simulation Using Google Earth

ERIC Educational Resources Information Center

Bodzin, Alec M.; Cirucci, Lori

2009-01-01

Google Earth (GE) is proving to be a valuable tool in the science classroom for understanding the environment and making responsible environmental decisions (Bodzin 2008). GE provides learners with a dynamic mapping experience using a simple interface with a limited range of functions. This interface makes geospatial analysis accessible and…

Assessing Place Location Knowledge Using a Virtual Globe

ERIC Educational Resources Information Center

Zhu, Liangfeng; Pan, Xin; Gao, Gongcheng

2016-01-01

Advances in the Google Earth virtual globe and the concomitant Keyhole Markup Language (KML) are providing educators with a convenient platform to cultivate and assess one's place location knowledge (PLK). This article presents a general framework and associated implementation methods for the online testing of PLK using Google Earth. The proposed…

Teachable Moment: Google Earth Takes Us There

ERIC Educational Resources Information Center

Williams, Ann; Davinroy, Thomas C.

2015-01-01

In the current educational climate, where clearly articulated learning objectives are required, it is clear that the spontaneous teachable moment still has its place. Authors Ann Williams and Thomas Davinroy think that instructors from almost any discipline can employ Google Earth as a tool to take advantage of teachable moments through the…

Three-dimensional slum urban reconstruction in Envisat and Google Earth Egypt

NASA Astrophysics Data System (ADS)

Marghany, M.; Genderen, J. v.

2014-02-01

This study aims to aim to investigate the capability of ENVISAT ASAR satellite and Google Earth data for three-dimensional (3-D) slum urban reconstruction in developed country such as Egypt. The main objective of this work is to utilize 3-D automatic detection algorithm for urban slum in ENVISAT ASAR and Google Erath images were acquired in Cairo, Egypt using Fuzzy B-spline algorithm. The results show that fuzzy algorithm is the best indicator for chaotic urban slum as it can discriminate them from its surrounding environment. The combination of Fuzzy and B-spline then used to reconstruct 3-D of urban slam. The results show that urban slums, road network, and infrastructures are perfectly discriminated. It can therefore be concluded that fuzzy algorithm is an appropriate algorithm for chaotic urban slum automatic detection in ENVSIAT ASAR and Google Earth data.

Feature Positioning on Google Street View Panoramas

NASA Astrophysics Data System (ADS)

Tsai, V. J. D.; Chang, C.-T.

2012-07-01

Location-based services (LBS) on web-based maps and images have come into real-time since Google launched its Street View imaging services in 2007. This research employs Google Maps API and Web Service, GAE for JAVA, AJAX, Proj4js, CSS and HTML in developing an internet platform for accessing the orientation parameters of Google Street View (GSV) panoramas in order to determine the three dimensional position of interest features that appear on two overlapping panoramas by geometric intersection. A pair of GSV panoramas was examined using known points located on the Library Building of National Chung Hsing University (NCHU) with the root-mean-squared errors of ±0.522m, ±1.230m, and ±5.779m for intersection and ±0.142m, ±1.558m, and ±5.733m for resection in X, Y, and h (elevation), respectively. Potential error sources in GSV positioning were analyzed and illustrated that the errors in Google provided GSV positional parameters dominate the errors in geometric intersection. The developed system is suitable for data collection in establishing LBS applications integrated with Google Maps and Google Earth in traffic sign and infrastructure inventory by adding automatic extraction and matching techniques for points of interest (POI) from GSV panoramas.

Google Earth Science

ERIC Educational Resources Information Center

Baird, William H.; Padgett, Clifford W.; Secrest, Jeffery A.

2015-01-01

Google Earth has made a wealth of aerial imagery available online at no cost to users. We examine some of the potential uses of that data in illustrating basic physics and astronomy, such as finding the local magnetic declination, using landmarks such as the Washington Monument and Luxor Obelisk as gnomons, and showing how airport runways get…

Using Geo-Spatial Technologies for Field Applications in Higher Geography Education

ERIC Educational Resources Information Center

Karatepe, Akif

2012-01-01

Today's important geo-spatial technologies, GIS (Geographic Information Systems), GPS (Global Positioning Systems) and Google Earth have been widely used in geography education. Transferring spatially oriented data taken by GPS to the GIS and Google Earth has provided great benefits in terms of showing the usage of spatial technologies for field…

Got the World on a Screen

ERIC Educational Resources Information Center

Adam, Anna; Mowers, Helen

2007-01-01

In this article, the authors discuss how Google Earth provides more than a geography lesson. For starters, Google Earth is perfect for teaching geography. Subscribe to Where in the World, for example, and have their students listen to podcast clues in a find-the-location game created by students worldwide. Clues relate to math (the population of…

A Virtual Tour of Plate Tectonics: Using Google Earth for Inquiry Investigations

ERIC Educational Resources Information Center

Mulvey, Bridget; Bell, Randy

2012-01-01

Google Earth is an exciting way to engage students in scientific inquiry--the foundation of science education standards and reforms. The National Science Education Standards identify inquiry as an active process that incorporates questioning, gathering and analyzing data, and thinking critically about the interplay of evidence and explanations.…

Teaching Topographic Map Skills and Geomorphology Concepts with Google Earth in a One-Computer Classroom

ERIC Educational Resources Information Center

Hsu, Hsiao-Ping; Tsai, Bor-Wen; Chen, Che-Ming

2018-01-01

Teaching high-school geomorphological concepts and topographic map reading entails many challenges. This research reports the applicability and effectiveness of Google Earth in teaching topographic map skills and geomorphological concepts, by a single teacher, in a one-computer classroom. Compared to learning via a conventional instructional…

Wind Wake Watcher v. 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Shawn

This software enables the user to produce Google Earth visualizations of turbine wake effects for wind farms. The visualizations are based on computations of statistical quantities that vary with wind direction and help quantify the effects on power production of upwind turbines on turbines in their wakes. The results of the software are plot images and kml files that can be loaded into Google Earth. The statistics computed are described in greater detail in the paper: S. Martin, C. H. Westergaard, and J. White (2016), Visualizing Wind Farm Wakes Using SCADA Data, in Wither Turbulence and Big Data in themore » 21st Century? Eds. A. Pollard, L. Castillo, L. Danaila, and M. Glauser. Springer, pgs. 231-254.« less

Interfaces Visualize Data for Airline Safety, Efficiency

NASA Technical Reports Server (NTRS)

2014-01-01

As the A-Train Constellation orbits Earth to gather data, NASA scientists and partners visualize, analyze, and communicate the information. To this end, Langley Research Center awarded SBIR funding to Fairfax, Virginia-based WxAnalyst Ltd. to refine the company's existing user interface for Google Earth to visualize data. Hawaiian Airlines is now using the technology to help manage its flights.

Spatio-temporal Change Patterns of Tropical Forests from 2000 to 2014 Using MOD09A1 Dataset

NASA Astrophysics Data System (ADS)

Qin, Y.; Xiao, X.; Dong, J.

2016-12-01

Large-scale deforestation and forest degradation in the tropical region have resulted in extensive carbon emissions and biodiversity loss. However, restricted by the availability of good-quality observations, large uncertainty exists in mapping the spatial distribution of forests and their spatio-temporal changes. In this study, we proposed a pixel- and phenology-based algorithm to identify and map annual tropical forests from 2000 to 2014, using the 8-day, 500-m MOD09A1 (v005) product, under the support of Google cloud computing (Google Earth Engine). A temporal filter was applied to reduce the random noises and to identify the spatio-temporal changes of forests. We then built up a confusion matrix and assessed the accuracy of the annual forest maps based on the ground reference interpreted from high spatial resolution images in Google Earth. The resultant forest maps showed the consistent forest/non-forest, forest loss, and forest gain in the pan-tropical zone during 2000 - 2014. The proposed algorithm showed the potential for tropical forest mapping and the resultant forest maps are important for the estimation of carbon emission and biodiversity loss.

Creating a Geo-Referenced Bibliography with Google Earth and Geocommons: The Coos Bay Bibliography

ERIC Educational Resources Information Center

Schmitt, Jenni; Butler, Barb

2012-01-01

We compiled a geo-referenced bibliography of research including theses, peer-reviewed articles, agency literature, and books having sample collection sites in and around Coos Bay, Oregon. Using Google Earth and GeoCommons we created a map that allows users such as visiting researchers, faculty, students, and local agencies to identify previous…

Planetary-Scale Geospatial Data Analysis Techniques in Google's Earth Engine Platform (Invited)

NASA Astrophysics Data System (ADS)

Hancher, M.

2013-12-01

Geoscientists have more and more access to new tools for large-scale computing. With any tool, some tasks are easy and other tasks hard. It is natural to look to new computing platforms to increase the scale and efficiency of existing techniques, but there is a more exiting opportunity to discover and develop a new vocabulary of fundamental analysis idioms that are made easy and effective by these new tools. Google's Earth Engine platform is a cloud computing environment for earth data analysis that combines a public data catalog with a large-scale computational facility optimized for parallel processing of geospatial data. The data catalog includes a nearly complete archive of scenes from Landsat 4, 5, 7, and 8 that have been processed by the USGS, as well as a wide variety of other remotely-sensed and ancillary data products. Earth Engine supports a just-in-time computation model that enables real-time preview during algorithm development and debugging as well as during experimental data analysis and open-ended data exploration. Data processing operations are performed in parallel across many computers in Google's datacenters. The platform automatically handles many traditionally-onerous data management tasks, such as data format conversion, reprojection, resampling, and associating image metadata with pixel data. Early applications of Earth Engine have included the development of Google's global cloud-free fifteen-meter base map and global multi-decadal time-lapse animations, as well as numerous large and small experimental analyses by scientists from a range of academic, government, and non-governmental institutions, working in a wide variety of application areas including forestry, agriculture, urban mapping, and species habitat modeling. Patterns in the successes and failures of these early efforts have begun to emerge, sketching the outlines of a new set of simple and effective approaches to geospatial data analysis.

An Agro-Climatological Early Warning Tool Based on the Google Earth Engine to Support Regional Food Security Analysis

NASA Astrophysics Data System (ADS)

Landsfeld, M. F.; Daudert, B.; Friedrichs, M.; Morton, C.; Hegewisch, K.; Husak, G. J.; Funk, C. C.; Peterson, P.; Huntington, J. L.; Abatzoglou, J. T.; Verdin, J. P.; Williams, E. L.

2015-12-01

The Famine Early Warning Systems Network (FEWS NET) focuses on food insecurity in developing nations and provides objective, evidence based analysis to help government decision-makers and relief agencies plan for and respond to humanitarian emergencies. The Google Earth Engine (GEE) is a platform provided by Google Inc. to support scientific research and analysis of environmental data in their cloud environment. The intent is to allow scientists and independent researchers to mine massive collections of environmental data and leverage Google's vast computational resources to detect changes and monitor the Earth's surface and climate. GEE hosts an enormous amount of satellite imagery and climate archives, one of which is the Climate Hazards Group Infrared Precipitation with Stations dataset (CHIRPS). The CHIRPS dataset is land based, quasi-global (latitude 50N-50S), 0.05 degree resolution, and has a relatively long term period of record (1981-present). CHIRPS is on a continuous monthly feed into the GEE as new data fields are generated each month. This precipitation dataset is a key input for FEWS NET monitoring and forecasting efforts. FEWS NET intends to leverage the GEE in order to provide analysts and scientists with flexible, interactive tools to aid in their monitoring and research efforts. These scientists often work in bandwidth limited regions, so lightweight Internet tools and services that bypass the need for downloading massive datasets to analyze them, are preferred for their work. The GEE provides just this type of service. We present a tool designed specifically for FEWS NET scientists to be utilized interactively for investigating and monitoring for agro-climatological issues. We are able to utilize the enormous GEE computing power to generate on-the-fly statistics to calculate precipitation anomalies, z-scores, percentiles and band ratios, and allow the user to interactively select custom areas for statistical time series comparisons and predictions.

Global positioning system & Google Earth in the investigation of an outbreak of cholera in a village of Bengaluru Urban district, Karnataka.

PubMed

Masthi, N R Ramesh; Madhusudan, M; Puthussery, Yannick P

2015-11-01

The global positioning system (GPS) technology along with Google Earth is used to measure (spatial map) the accurate distribution of morbidity, mortality and planning of interventions in the community. We used this technology to find out its role in the investigation of a cholera outbreak, and also to identify the cause of the outbreak. This study was conducted in a village near Bengaluru, Karnataka in June 2013 during a cholera outbreak. House-to-house survey was done to identify acute watery diarrhoea cases. A hand held GPS receiver was used to record north and east coordinates of the households of cases and these values were subsequently plotted on Google Earth map. Water samples were collected from suspected sources for microbiological analysis. A total of 27 cases of acute watery diarrhoea were reported. Fifty per cent of cases were in the age group of 14-44 yr and one death was reported. GPS technology and Google Earth described the accurate location of household of cases and spot map generated showed clustering of cases around the suspected water sources. The attack rate was 6.92 per cent and case fatality rate was 3.7 per cent. Water samples collected from suspected sources showed the presence of Vibrio cholera O1 Ogawa. GPS technology and Google Earth were easy to use, helpful to accurately pinpoint the location of household of cases, construction of spot map and follow up of cases. Outbreak was found to be due to contamination of drinking water sources.

Accuracy comparison in mapping water bodies using Landsat images and Google Earth Images

NASA Astrophysics Data System (ADS)

Zhou, Z.; Zhou, X.

2016-12-01

A lot of research has been done for the extraction of water bodies with multiple satellite images. The Water Indexes with the use of multi-spectral images are the mostly used methods for the water bodies' extraction. In order to extract area of water bodies from satellite images, accuracy may depend on the spatial resolution of images and relative size of the water bodies. To quantify the impact of spatial resolution and size (major and minor lengths) of the water bodies on the accuracy of water area extraction, we use Georgetown Lake, Montana and coalbed methane (CBM) water retention ponds in the Montana Powder River Basin as test sites to evaluate the impact of spatial resolution and the size of water bodies on water area extraction. Data sources used include Landsat images and Google Earth images covering both large water bodies and small ponds. Firstly we used water indices to extract water coverage from Landsat images for both large lake and small ponds. Secondly we used a newly developed visible-index method to extract water coverage from Google Earth images covering both large lake and small ponds. Thirdly, we used the image fusion method in which the Google Earth Images are fused with multi-spectral Landsat images to obtain multi-spectral images of the same high spatial resolution as the Google earth images. The actual area of the lake and ponds are measured using GPS surveys. Results will be compared and the optimal method will be selected for water body extraction.

The Effect of Google Earth and Wiki Models on Oral Presentation Skills of University EFL Learners

ERIC Educational Resources Information Center

Awada, Ghada; Diab, Hassan B.

2018-01-01

This article reports the results of an experimental study that investigated the effectiveness of Google Earth and Wiki tools in improving the oral presentation skills of English as a Foreign Language (EFL) learners and boosting their motivation for learning. The participants (n =81) are enrolled in writing classes at two English-medium…

Utilizing Google Earth to Teach Students about Global Oil Spill Disasters

ERIC Educational Resources Information Center

Guertin, Laura; Neville, Sara

2011-01-01

The United States is currently experiencing its worst man-made environmental disaster, the BP Deepwater Horizon oil leak. The Gulf of Mexico oil spill is severe in its impact, but it is only one of several global oil spill disasters in history. Students can utilize the technology of Google Earth to explore the spatial and temporal distribution of…

Vizualization of Arctic Landscapes in the Geoinformation System

NASA Astrophysics Data System (ADS)

Panidi, E. A.; Tsepelev, V. Yu.; Bobkov, A. A.

2010-12-01

In order to investigate the long-scale dynamics of an ice cover, authors suggest to use the geoinformation system (GIS) which allows to conduct the operative and historical analysis of the Polar Region water-ice landscapes variability. Such GIS should include longterm monthly average fields of sea ice, hydrological and atmospheric characters. All collected data and results of their processing have been structured in ArcGISTM . For presentation in the INTERNET resources all datasets were transformed to the open format KML for using in the virtual reality of Google EarthTM . The double component system elaborating on the base of ArcGIS and Google Earth allows to make accumulation, processing and joint synchronous and asynchronous analysis of data and provide wide circle of remote users with accessibility of visual datasets analysis.

Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti.

PubMed

Wampler, Peter J; Rediske, Richard R; Molla, Azizur R

2013-01-18

A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This method provides an important technique that can be applied to other developing countries where a randomized study design is needed but infrastructure is lacking to implement more traditional participant selection methods.

3D Orbit Visualization for Earth-Observing Missions

NASA Technical Reports Server (NTRS)

Jacob, Joseph C.; Plesea, Lucian; Chafin, Brian G.; Weiss, Barry H.

2011-01-01

This software visualizes orbit paths for the Orbiting Carbon Observatory (OCO), but was designed to be general and applicable to any Earth-observing mission. The software uses the Google Earth user interface to provide a visual mechanism to explore spacecraft orbit paths, ground footprint locations, and local cloud cover conditions. In addition, a drill-down capability allows for users to point and click on a particular observation frame to pop up ancillary information such as data product filenames and directory paths, latitude, longitude, time stamp, column-average dry air mole fraction of carbon dioxide, and solar zenith angle. This software can be integrated with the ground data system for any Earth-observing mission to automatically generate daily orbit path data products in Google Earth KML format. These KML data products can be directly loaded into the Google Earth application for interactive 3D visualization of the orbit paths for each mission day. Each time the application runs, the daily orbit paths are encapsulated in a KML file for each mission day since the last time the application ran. Alternatively, the daily KML for a specified mission day may be generated. The application automatically extracts the spacecraft position and ground footprint geometry as a function of time from a daily Level 1B data product created and archived by the mission s ground data system software. In addition, ancillary data, such as the column-averaged dry air mole fraction of carbon dioxide and solar zenith angle, are automatically extracted from a Level 2 mission data product. Zoom, pan, and rotate capability are provided through the standard Google Earth interface. Cloud cover is indicated with an image layer from the MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the Aqua satellite, which is automatically retrieved from JPL s OnEarth Web service.

Google Sky: A Digital View of the Night Sky

NASA Astrophysics Data System (ADS)

Connolly, A. Scranton, R.; Ornduff, T.

2008-11-01

From its inception Astronomy has been a visual science, from careful observations of the sky using the naked eye, to the use of telescopes and photographs to map the distribution of stars and galaxies, to the current era of digital cameras that can image the sky over many decades of the electromagnetic spectrum. Sky in Google Earth (http://earth.google.com) and Google Sky (http://www.google.com/sky) continue this tradition, providing an intuitive visual interface to some of the largest astronomical imaging surveys of the sky. Streaming multi-color imagery, catalogs, time domain data, as well as annotating interesting astronomical sources and events with placemarks, podcasts and videos, Sky provides a panchromatic view of the universe accessible to anyone with a computer. Beyond a simple exploration of the sky Google Sky enables users to create and share content with others around the world. With an open interface available on Linux, Mac OS X and Windows, and translations of the content into over 20 different languages we present Sky as the embodiment of a virtual telescope for discovery and sharing the excitement of astronomy and science as a whole.

Elevation data fitting and precision analysis of Google Earth in road survey

NASA Astrophysics Data System (ADS)

Wei, Haibin; Luan, Xiaohan; Li, Hanchao; Jia, Jiangkun; Chen, Zhao; Han, Leilei

2018-05-01

Objective: In order to improve efficiency of road survey and save manpower and material resources, this paper intends to apply Google Earth to the feasibility study stage of road survey and design. Limited by the problem that Google Earth elevation data lacks precision, this paper is focused on finding several different fitting or difference methods to improve the data precision, in order to make every effort to meet the accuracy requirements of road survey and design specifications. Method: On the basis of elevation difference of limited public points, any elevation difference of the other points can be fitted or interpolated. Thus, the precise elevation can be obtained by subtracting elevation difference from the Google Earth data. Quadratic polynomial surface fitting method, cubic polynomial surface fitting method, V4 interpolation method in MATLAB and neural network method are used in this paper to process elevation data of Google Earth. And internal conformity, external conformity and cross correlation coefficient are used as evaluation indexes to evaluate the data processing effect. Results: There is no fitting difference at the fitting point while using V4 interpolation method. Its external conformity is the largest and the effect of accuracy improvement is the worst, so V4 interpolation method is ruled out. The internal and external conformity of the cubic polynomial surface fitting method both are better than those of the quadratic polynomial surface fitting method. The neural network method has a similar fitting effect with the cubic polynomial surface fitting method, but its fitting effect is better in the case of a higher elevation difference. Because the neural network method is an unmanageable fitting model, the cubic polynomial surface fitting method should be mainly used and the neural network method can be used as the auxiliary method in the case of higher elevation difference. Conclusions: Cubic polynomial surface fitting method can obviously improve data precision of Google Earth. The error of data in hilly terrain areas meets the requirement of specifications after precision improvement and it can be used in feasibility study stage of road survey and design.

How Would You Move Mount Fuji - And Why Would You Want To?

NASA Astrophysics Data System (ADS)

de Paor, D. G.

2008-12-01

According to author William Poundstone, "How Would You Move Mt Fuji?" typifies the kind of question that corporations such as Microsoft are wont to ask job applicants in order to test their lateral thinking skills. One answer (albeit not one that would necessarily secure a job at Microsoft) is: "With Google Earth and a Macintosh or PC." The answer to the more profound follow-up question "Why Would You Want To?" is hinted at by one of the great quotations of earth science, namely Charles Lyell's proposition that "The Present Is Key to the Past." Google Earth is a phenomenally powerful tool for visualizing today's earth, ocean, and atmosphere. With the aid of Google SketchUp, that visualization can be extended to reconstruct the past using relocated samples of present-day landscapes and environments as models of paleo-DEM and paleogeography. Volcanoes are particularly useful models because their self similar growth can be simulated by changing KML altitude tags within a timespan, but numerous other landforms and geologic structures serve as useful keys to the past. Examples range in scale from glaciers and fault scarps to island arcs and mountain ranges. The ability to generate a paleo-terrain model in Google Earth brings us one step closer to a truly four- dimensional, interactive geological map of the world throughout time.

GeneOnEarth: fitting genetic PC plots on the globe.

PubMed

Torres-Sánchez, Sergio; Medina-Medina, Nuria; Gignoux, Chris; Abad-Grau, María M; González-Burchard, Esteban

2013-01-01

Principal component (PC) plots have become widely used to summarize genetic variation of individuals in a sample. The similarity between genetic distance in PC plots and geographical distance has shown to be quite impressive. However, in most situations, individual ancestral origins are not precisely known or they are heterogeneously distributed; hence, they are hardly linked to a geographical area. We have developed GeneOnEarth, a user-friendly web-based tool to help geneticists to understand whether a linear isolation-by-distance model may apply to a genetic data set; thus, genetic distances among a set of individuals resemble geographical distances among their origins. Its main goal is to allow users to first apply a by-view Procrustes method to visually learn whether this model holds. To do that, the user can choose the exact geographical area from an on line 2D or 3D world map by using, respectively, Google Maps or Google Earth, and rotate, flip, and resize the images. GeneOnEarth can also compute the optimal rotation angle using Procrustes analysis and assess statistical evidence of similarity when a different rotation angle has been chosen by the user. An online version of GeneOnEarth is available for testing and using purposes at http://bios.ugr.es/GeneOnEarth.

Results of Prospecting of Impact Craters in Morocco

NASA Astrophysics Data System (ADS)

Chaabout, S.; Chennaoui Aoudjehane, H.; Reimold, W. U.; Baratoux, D.

2014-09-01

This work is based to use satellite images of Google Earth and Yahoo-Maps scenes; we examined the surface of our country to be able to locate the structures that have a circular morphology such as impact craters, which potentially could be.

Using Google Streetview Panoramic Imagery for Geoscience Education

NASA Astrophysics Data System (ADS)

De Paor, D. G.; Dordevic, M. M.

2014-12-01

Google Streetview is a feature of Google Maps and Google Earth that allows viewers to switch from map or satellite view to 360° panoramic imagery recorded close to the ground. Most panoramas are recorded by Google engineers using special cameras mounted on the roofs of cars. Bicycles, snowmobiles, and boats have also been used and sometimes the camera has been mounted on a backpack for off-road use by hikers and skiers or attached to scuba-diving gear for "Underwater Streetview (sic)." Streetview panoramas are linked together so that the viewer can change viewpoint by clicking forward and reverse buttons. They therefore create a 4-D touring effect. As part of the GEODE project ("Google Earth for Onsite and Distance Education"), we are experimenting with the use of Streetview imagery for geoscience education. Our web-based test application allows instructors to select locations for students to study. Students are presented with a set of questions or tasks that they must address by studying the panoramic imagery. Questions include identification of rock types, structures such as faults, and general geological setting. The student view is locked into Streetview mode until they submit their answers, whereupon the map and satellite views become available, allowing students to zoom out and verify their location on Earth. Student learning is scaffolded by automatic computerized feedback. There are lots of existing Streetview panoramas with rich geological content. Additionally, instructors and members of the general public can create panoramas, including 360° Photo Spheres, by stitching images taken with their mobiles devices and submitting them to Google for evaluation and hosting. A multi-thousand-dollar, multi-directional camera and mount can be purchased from DIY-streetview.com. This allows power users to generate their own high-resolution panoramas. A cheaper, 360° video camera is soon to be released according to geonaute.com. Thus there are opportunities for geoscience educators both to use existing Streetview imagery and to generate new imagery for specific locations of geological interest. The GEODE team includes the authors and: H. Almquist, C. Bentley, S. Burgin, C. Cervato, G. Cooper, P. Karabinos, T. Pavlis, J. Piatek, B. Richards, J. Ryan, R. Schott, K. St. John, B. Tewksbury, and S. Whitmeyer.

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

NASA Technical Reports Server (NTRS)

Hegde, Mahabaleshwara; Strub, Richard F.; Lynnes, Christopher S.; Fang, Hongliang; Teng, William

2008-01-01

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies.

Finding the Shape of Space

DTIC Science & Technology

2011-07-01

currently valid OMB control number. 1. REPORT DATE JUL 2011 2. REPORT TYPE 3. DATES COVERED 00-00-2011 to 00-00-2011 4. TITLE AND SUBTITLE...1 Lt Col Christopher C. Shannon Maj Tosha N. Meredith 2 GOOGLE EARTH TUBE: PROSPECTS FOR FULL MOTION VIDEO FROM SPACE . . . . . . . 5...Google Earth Tube,” a virtual environment that provides an extraordinary amount of information to whoever accesses it, sets the stage for improved

Optimum Antenna Configuration for Maximizing Access Point Range of an IEEE 802.11 Wireless Mesh Network in Support of Multi-Mission Operations Relative to Hastily Formed Scalable Deployments

DTIC Science & Technology

2007-09-01

Configuration Consideration ...........................54 C. MAE NGAT DAM, CHIANG MAI , THAILAND, FIELD EXPERIMENT...2006 802.11 Network Topology Mae Ngat Dam, Chiang Mai , Thailand.......................39 Figure 31. View of COASTS 2006 802.11 Topology...Requirements (Background From Google Earth).....62 Figure 44. Mae Ngat Dam, Chiang Mai , Thailand (From Google Earth

Naive (commonsense) geography and geobrowser usability after ten years of Google Earth

NASA Astrophysics Data System (ADS)

Hamerlinck, J. D.

2016-04-01

In 1995, the concept of ‘naive geography’ was formally introduced as an area of cognitive geographic information science representing ‘the body of knowledge that people have about the surrounding geographic world’ and reflecting ‘the way people think and reason about geographic space and time, both consciously and subconsciously’. The need to incorporate such commonsense knowledge and reasoning into design of geospatial technologies was identified but faced challenges in formalizing these relationships and processes in software implementation. Ten years later, the Google Earth geobrowser was released, marking the beginning of a new era of open access to, and application of, geographic data and information in society. Fast-forward to today, and the opportunity presents itself to take stock of twenty years of naive geography and a decade of the ubiquitous virtual globe. This paper introduces an ongoing research effort to explore the integration of naive (or commonsense) geography concepts in the Google Earth geobrowser virtual globe and their possible impact on Google Earth's usability, utility, and usefulness. A multi-phase methodology is described, combining usability reviews and usability testing with use-case scenarios involving the U.S.-Canadian Yellowstone to Yukon Initiative. Initial progress on a usability review combining cognitive walkthroughs and heuristics evaluation is presented.

Using Google Earth to Explore Strain Rate Models of Southern California

NASA Astrophysics Data System (ADS)

Richard, G. A.; Bell, E. A.; Holt, W. E.

2007-12-01

A series of strain rate models for the Transverse Ranges of southern California were developed based on Quaternary fault slip data and geodetic data from high precision GPS stations in southern California. Pacific-North America velocity boundary conditions are applied for all models. Topography changes are calculated using the model dilatation rates, which predict crustal thickness changes under the assumption of Airy isostasy and a specified rate of crustal volume loss through erosion. The models were designed to produce graphical and numerical output representing the configuration of the region from 3 million years ago to 3 million years into the future at intervals of 50 thousand years. Using a North American reference frame, graphical output for the topography and faults and numerical output for locations of faults and points on the crust marked by the locations on cities were used to create data in KML format that can be used in Google Earth to represent time intervals of 50 thousand years. As markers familiar to students, the cities provide a geographic context that can be used to quantify crustal movement, using the Google Earth ruler tool. By comparing distances that markers for selected cities have moved in various parts of the region, students discover that the greatest amount of crustal deformation has occurred in the vicinity of the boundary between the North American and Pacific plates. Students can also identify areas of compression or extension by finding pairs of city markers that have converged or diverged, respectively, over time. The Google Earth layers also reveal that faults that are not parallel to the plate boundary have tended to rotate clockwise due to the right lateral motion along the plate boundary zone. KML TimeSpan markup was added to two versions of the model, enabling the layers to be displayed in an automatic sequenced loop for a movie effect. The data is also available as QuickTime (.mov) and Graphics Interchange Format (.gif) animations and in ESRI Shapefile format.

Google Earth Engine derived areal extents to infer elevation variation of lakes and reservoirs

NASA Astrophysics Data System (ADS)

Nguy-Robertson, Anthony; May, Jack; Dartevelle, Sebastien; Griffin, Sean; Miller, Justin; Tetrault, Robert; Birkett, Charon; Lucero, Eileen; Russo, Tess; Zentner, Matthew

2017-04-01

Monitoring water supplies is important for identifying potential national security issues before they begin. As a means to estimate lake and reservoir storage for sites without reliable water stage data, this study defines correlations between water body levels from hypsometry curves based on in situ gauge station and altimeter data (i.e. TOPEX/Poseidon, Jason series) and sensor areal extents observed in historic multispectral (i.e. MODIS and Landsat TM/ETM+/OLI) imagery. Water levels measured using in situ observations and altimeters, when in situ data were unavailable, were used to estimate the relationship between water elevation and surface area for 18 sites globally. Altimeters were generally more accurate (RMSE: 0.40 - 0.49 m) for estimating in situ lake elevations from Iraq and Afghanistan than the modeled elevation data using multispectral sensor areal extents: Landsat (RMSE: 0.25 - 1.5 m) and MODIS (RMSE 0.53 - 3.0 m). Correlations between altimeter data and Landsat imagery processed with Google Earth Engine confirmed similar relationships exists for a broader range of lakes without reported in situ data across the globe (RMSE: 0.24 - 1.6 m). Thus, while altimetry is still preferred to an areal extent model, lake surface area derived with Google Earth Engine can be used as a reasonable proxy for lake storage, expanding the number of observable lakes beyond the current constellation of altimeters and in situ gauges.

Improved discrimination among similar agricultural plots using red-and-green-based pseudo-colour imaging

NASA Astrophysics Data System (ADS)

Doi, Ryoichi

2016-04-01

The effects of a pseudo-colour imaging method were investigated by discriminating among similar agricultural plots in remote sensing images acquired using the Airborne Visible/Infrared Imaging Spectrometer (Indiana, USA) and the Landsat 7 satellite (Fergana, Uzbekistan), and that provided by GoogleEarth (Toyama, Japan). From each dataset, red (R)-green (G)-R-G-blue yellow (RGrgbyB), and RGrgby-1B pseudo-colour images were prepared. From each, cyan, magenta, yellow, key black, L*, a*, and b* derivative grayscale images were generated. In the Airborne Visible/Infrared Imaging Spectrometer image, pixels were selected for corn no tillage (29 pixels), corn minimum tillage (27), and soybean (34) plots. Likewise, in the Landsat 7 image, pixels representing corn (73 pixels), cotton (110), and wheat (112) plots were selected, and in the GoogleEarth image, those representing soybean (118 pixels) and rice (151) were selected. When the 14 derivative grayscale images were used together with an RGB yellow grayscale image, the overall classification accuracy improved from 74 to 94% (Airborne Visible/Infrared Imaging Spectrometer), 64 to 83% (Landsat), or 77 to 90% (GoogleEarth). As an indicator of discriminatory power, the kappa significance improved 1018-fold (Airborne Visible/Infrared Imaging Spectrometer) or greater. The derivative grayscale images were found to increase the dimensionality and quantity of data. Herein, the details of the increases in dimensionality and quantity are further analysed and discussed.

Generating Southern Africa Precipitation Forecast Using the FEWS Engine, a New Application for the Google Earth Engine

NASA Astrophysics Data System (ADS)

Landsfeld, M. F.; Hegewisch, K.; Daudert, B.; Morton, C.; Husak, G. J.; Friedrichs, M.; Funk, C. C.; Huntington, J. L.; Abatzoglou, J. T.; Verdin, J. P.

2016-12-01

The Famine Early Warning Systems Network (FEWS NET) focuses on food insecurity in developing nations and provides objective, evidence-based analysis to help government decision-makers and relief agencies plan for and respond to humanitarian emergencies. The network of FEWS NET analysts and scientists require flexible, interactive tools to aid in their monitoring and research efforts. Because they often work in bandwidth-limited regions, lightweight Internet tools and services that bypass the need for downloading massive datasets are preferred for their work. To support food security analysis FEWS NET developed a custom interface for the Google Earth Engine (GEE). GEE is a platform developed by Google to support scientific analysis of environmental data in their cloud computing environment. This platform allows scientists and independent researchers to mine massive collections of environmental data, leveraging Google's vast computational resources for purposes of detecting changes and monitoring the Earth's surface and climate. GEE hosts an enormous amount of satellite imagery and climate archives, one of which is the Climate Hazards Group Infrared Precipitation with Stations dataset (CHIRPS). CHIRPS precipitation dataset is a key input for FEWS NET monitoring and forecasting efforts. In this talk we introduce the FEWS Engine interface. We present an application that highlights the utility of FEWS Engine for forecasting the upcoming seasonal precipitation of southern Africa. Specifically, the current state of ENSO is assessed and used to identify similar historical seasons. The FEWS Engine compositing tool is used to examine rainfall and other environmental data for these analog seasons. The application illustrates the unique benefits of using FEWS Engine for on-the-fly food security scenario development.

Visualization of High-Resolution LiDAR Topography in Google Earth

NASA Astrophysics Data System (ADS)

Crosby, C. J.; Nandigam, V.; Arrowsmith, R.; Blair, J. L.

2009-12-01

The growing availability of high-resolution LiDAR (Light Detection And Ranging) topographic data has proven to be revolutionary for Earth science research. These data allow scientists to study the processes acting on the Earth’s surfaces at resolutions not previously possible yet essential for their appropriate representation. In addition to their utility for research, the data have also been recognized as powerful tools for communicating earth science concepts for education and outreach purposes. Unfortunately, the massive volume of data produced by LiDAR mapping technology can be a barrier to their use. To facilitate access to these powerful data for research and educational purposes, we have been exploring the use of Keyhole Markup Language (KML) and Google Earth to deliver LiDAR-derived visualizations. The OpenTopography Portal (http://www.opentopography.org/) is a National Science Foundation-funded facility designed to provide access to Earth science-oriented LiDAR data. OpenTopography hosts a growing collection of LiDAR data for a variety of geologic domains, including many of the active faults in the western United States. We have found that the wide spectrum of LiDAR users have variable scientific applications, computing resources, and technical experience and thus require a data distribution system that provides various levels of access to the data. For users seeking a synoptic view of the data, and for education and outreach purposes, delivering full-resolution images derived from LiDAR topography into the Google Earth virtual globe is powerful. The virtual globe environment provides a freely available and easily navigated viewer and enables quick integration of the LiDAR visualizations with imagery, geographic layers, and other relevant data available in KML format. Through region-dependant network linked KML, OpenTopography currently delivers over 20 GB of LiDAR-derived imagery to users via simple, easily downloaded KMZ files hosted at the Portal. This method provides seamlessly access to hillshaded imagery for both bare earth and first return terrain models with various angles of illumination. Seamless access to LiDAR-derived imagery in Google Earth has proven to be the most popular product available in the OpenTopography Portal. The hillshade KMZ files have been downloaded over 3000 times by users ranging from earthquake scientists to K-12 educators who wish to introduce cutting edge real world data into their earth science lessons. OpenTopography also provides dynamically generated KMZ visualizations of LiDAR data products produced when users choose to use the OpenTopography point cloud access and processing system. These Google Earth compatible products allow users to quickly visualize the custom terrain products they have generated without the burden of loading the data into a GIS environment. For users who have installed the Google Earth browser plug-in, these visualizations can be launched directly from the OpenTopography results page and viewed directly in the browser.

3D Online Visualization and Synergy of NASA A-Train Data Using Google Earth

NASA Technical Reports Server (NTRS)

Chen, Aijun; Kempler, Steven; Leptoukh, Gregory; Smith, Peter

2010-01-01

This poster presentation reviews the use of Google Earth to assist in three dimensional online visualization of NASA Earth science and geospatial data. The NASA A-Train satellite constellation is a succession of seven sun-synchronous orbit satellites: (1) OCO-2 (Orbiting Carbon Observatory) (will launch in Feb. 2013), (2) GCOM-W1 (Global Change Observation Mission), (3) Aqua, (4) CloudSat, (5) CALIPSO (Cloud-Aerosol Lidar & Infrared Pathfinder Satellite Observations), (6) Glory, (7) Aura. The A-Train makes possible synergy of information from multiple resources, so more information about earth condition is obtained from the combined observations than would be possible from the sum of the observations taken independently

Web GIS in practice V: 3-D interactive and real-time mapping in Second Life

PubMed Central

Boulos, Maged N Kamel; Burden, David

2007-01-01

This paper describes technologies from Daden Limited for geographically mapping and accessing live news stories/feeds, as well as other real-time, real-world data feeds (e.g., Google Earth KML feeds and GeoRSS feeds) in the 3-D virtual world of Second Life, by plotting and updating the corresponding Earth location points on a globe or some other suitable form (in-world), and further linking those points to relevant information and resources. This approach enables users to visualise, interact with, and even walk or fly through, the plotted data in 3-D. Users can also do the reverse: put pins on a map in the virtual world, and then view the data points on the Web in Google Maps or Google Earth. The technologies presented thus serve as a bridge between mirror worlds like Google Earth and virtual worlds like Second Life. We explore the geo-data display potential of virtual worlds and their likely convergence with mirror worlds in the context of the future 3-D Internet or Metaverse, and reflect on the potential of such technologies and their future possibilities, e.g. their use to develop emergency/public health virtual situation rooms to effectively manage emergencies and disasters in real time. The paper also covers some of the issues associated with these technologies, namely user interface accessibility and individual privacy. PMID:18042275

Real-time bus location monitoring using Arduino

NASA Astrophysics Data System (ADS)

Ibrahim, Mohammad Y. M.; Audah, Lukman

2017-09-01

The Internet of Things (IoT) is the network of objects, such as a vehicles, mobile devices, and buildings that have electronic components, software, and network connectivity that enable them to collect data, run commands, and be controlled through the Internet. Controlling physical items from the Internet will increase efficiency and save time. The growing number of devices used by people increases the practicality of having IoT devices on the market. The IoT is also an opportunity to develop products that can save money and time and increase work efficiency. Initially, they need more efficiency for real-time bus location systems, especially in university campuses. This system can easily find the accurate locations of and distances between each bus stop and the estimated time to reach a new location. This system has been separated into two parts, which are the hardware and the software. The hardware parts are the Arduino Uno and the Global Positioning System (GPS), while Google Earth and GpsGate are the software parts. The GPS continuously takes input data from the satellite and stores the latitude and longitude values in the Arduino Uno. If we want to track the vehicle, we need to send the longitude and latitude as a message to the Google Earth software to convert these into maps for navigation. Once the Arduino Uno is activated, it takes the last received latitude and longitude positions' values from GpsGate and sends a message to Google Earth. Once the message has been sent to Google Earth, the current location will be shown, and navigation will be activated automatically. Then it will be broadcast using ManyCam, Google+ Hangouts, and YouTube, as well as Facebook, and appear to users. The additional features use Google Forms for determining problems faced by students, who can also take immediate action against the responsible department. Then after several successful simulations, the results will be shown in real time on a map.

Multi-source Geospatial Data Analysis with Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T.

2014-12-01

The Google Earth Engine platform is a cloud computing environment for data analysis that combines a public data catalog with a large-scale computational facility optimized for parallel processing of geospatial data. The data catalog is a multi-petabyte archive of georeferenced datasets that include images from Earth observing satellite and airborne sensors (examples: USGS Landsat, NASA MODIS, USDA NAIP), weather and climate datasets, and digital elevation models. Earth Engine supports both a just-in-time computation model that enables real-time preview and debugging during algorithm development for open-ended data exploration, and a batch computation mode for applying algorithms over large spatial and temporal extents. The platform automatically handles many traditionally-onerous data management tasks, such as data format conversion, reprojection, and resampling, which facilitates writing algorithms that combine data from multiple sensors and/or models. Although the primary use of Earth Engine, to date, has been the analysis of large Earth observing satellite datasets, the computational platform is generally applicable to a wide variety of use cases that require large-scale geospatial data analyses. This presentation will focus on how Earth Engine facilitates the analysis of geospatial data streams that originate from multiple separate sources (and often communities) and how it enables collaboration during algorithm development and data exploration. The talk will highlight current projects/analyses that are enabled by this functionality.https://earthengine.google.org

Moving beyond a Google Search: Google Earth, SketchUp, Spreadsheet, and More

ERIC Educational Resources Information Center

Siegle, Del

2007-01-01

Google has been the search engine of choice for most Web surfers for the past half decade. More recently, the creative founders of the popular search engine have been busily creating and testing a variety of useful products that will appeal to gifted learners of varying ages. The purpose of this paper is to share information about three of these…

Graphical overview and navigation of electronic health records in a prototyping environment using Google Earth and openEHR archetypes.

PubMed

Sundvall, Erik; Nyström, Mikael; Forss, Mattias; Chen, Rong; Petersson, Håkan; Ahlfeldt, Hans

2007-01-01

This paper describes selected earlier approaches to graphically relating events to each other and to time; some new combinations are also suggested. These are then combined into a unified prototyping environment for visualization and navigation of electronic health records. Google Earth (GE) is used for handling display and interaction of clinical information stored using openEHR data structures and 'archetypes'. The strength of the approach comes from GE's sophisticated handling of detail levels, from coarse overviews to fine-grained details that has been combined with linear, polar and region-based views of clinical events related to time. The system should be easy to learn since all the visualization styles can use the same navigation. The structured and multifaceted approach to handling time that is possible with archetyped openEHR data lends itself well to visualizing and integration with openEHR components is provided in the environment.

Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis

NASA Astrophysics Data System (ADS)

Moore, R. T.; Hansen, M. C.

2011-12-01

Google Earth Engine is a new technology platform that enables monitoring and measurement of changes in the earth's environment, at planetary scale, on a large catalog of earth observation data. The platform offers intrinsically-parallel computational access to thousands of computers in Google's data centers. Initial efforts have focused primarily on global forest monitoring and measurement, in support of REDD+ activities in the developing world. The intent is to put this platform into the hands of scientists and developing world nations, in order to advance the broader operational deployment of existing scientific methods, and strengthen the ability for public institutions and civil society to better understand, manage and report on the state of their natural resources. Earth Engine currently hosts online nearly the complete historical Landsat archive of L5 and L7 data collected over more than twenty-five years. Newly-collected Landsat imagery is downloaded from USGS EROS Center into Earth Engine on a daily basis. Earth Engine also includes a set of historical and current MODIS data products. The platform supports generation, on-demand, of spatial and temporal mosaics, "best-pixel" composites (for example to remove clouds and gaps in satellite imagery), as well as a variety of spectral indices. Supervised learning methods are available over the Landsat data catalog. The platform also includes a new application programming framework, or "API", that allows scientists access to these computational and data resources, to scale their current algorithms or develop new ones. Under the covers of the Google Earth Engine API is an intrinsically-parallel image-processing system. Several forest monitoring applications powered by this API are currently in development and expected to be operational in 2011. Combining science with massive data and technology resources in a cloud-computing framework can offer advantages of computational speed, ease-of-use and collaboration, as well as transparency in data and methods. Methods developed for global processing of MODIS data to map land cover are being adopted for use with Landsat data. Specifically, the MODIS Vegetation Continuous Field product methodology has been applied for mapping forest extent and change at national scales using Landsat time-series data sets. Scaling this method to continental and global scales is enabled by Google Earth Engine computing capabilities. By combining the supervised learning VCF approach with the Landsat archive and cloud computing, unprecedented monitoring of land cover dynamics is enabled.

Understanding "Change" through Spatial Thinking Using Google Earth in Secondary Geography

ERIC Educational Resources Information Center

Xiang, X.; Liu, Y.

2017-01-01

Understanding geographic changes has become an indispensable element in geography education. Describing and analyzing changes in space require spatial thinking skills emphasized in geography curriculum but often pose challenges for secondary school students. This school-based research targets a specific strand of spatial thinking skills and…

Using Google Earth in Marine Research and Operational Decision Support

NASA Astrophysics Data System (ADS)

Blower, J. D.; Bretherton, D.; Haines, K.; Liu, C.; Rawlings, C.; Santokhee, A.; Smith, I.

2006-12-01

A key advantage of Virtual Globes ("geobrowsers") such as Google Earth is that they can display many different geospatial data types at a huge range of spatial scales. In this demonstration and poster display we shall show how marine data from disparate sources can be brought together in a geobrowser in order to support both scientific research and operational search and rescue activities. We have developed the Godiva2 interactive website for browsing and exploring marine data, mainly output from supercomputer analyses and predictions of ocean circulation. The user chooses a number of parameters (e.g. sea temperature at 100m depth on 1st July 2006) and can load an image of the resulting data in Google Earth. Through the use of an automatically-refreshing NetworkLink the user can explore the whole globe at a very large range of spatial scales: the displayed data will automatically be refreshed to show data at increasingly fine resolution as the user zooms in. This is a valuable research tool for exploring these terabyte- scale datasets. Many coastguard organizations around the world use SARIS, a software application produced by BMT Cordah Ltd., to predict the drift pattern of objects in the sea in order to support search and rescue operations. Different drifting objects have different trajectories depending on factors such as their buoyancy and windage and so a computer model, supported by meteorological and oceanographic data, is needed to help rescuers locate their targets. We shall demonstrate how Google Earth is used to display output from the SARIS model (including the search target location and associated error polygon) alongside meteorological data (wind vectors) and oceanographic data (sea temperature, surface currents) from Godiva2 in order to support decision-making. We shall also discuss the limitations of using Google Earth in this context: these include the difficulties of working with time- dependent data and the need to access data securely. essc.ac.uk:8080/Godiva2

Climate Engine - Monitoring Drought with Google Earth Engine

NASA Astrophysics Data System (ADS)

Hegewisch, K.; Daudert, B.; Morton, C.; McEvoy, D.; Huntington, J. L.; Abatzoglou, J. T.

2016-12-01

Drought has adverse effects on society through reduced water availability and agricultural production and increased wildfire risk. An abundance of remotely sensed imagery and climate data are being collected in near-real time that can provide place-based monitoring and early warning of drought and related hazards. However, in an era of increasing wealth of earth observations, tools that quickly access, compute, and visualize archives, and provide answers at relevant scales to better inform decision-making are lacking. We have developed ClimateEngine.org, a web application that uses Google's Earth Engine platform to enable users to quickly compute and visualize real-time observations. A suite of drought indices allow us to monitor and track drought from local (30-meters) to regional scales and contextualize current droughts within the historical record. Climate Engine is currently being used by U.S. federal agencies and researchers to develop baseline conditions and impact assessments related to agricultural, ecological, and hydrological drought. Climate Engine is also working with the Famine Early Warning Systems Network (FEWS NET) to expedite monitoring agricultural drought over broad areas at risk of food insecurity globally.

Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images

NASA Astrophysics Data System (ADS)

Ghaffarian, Saman; Ghaffarian, Salar

2014-11-01

This paper proposes an improved FastICA model named as Purposive FastICA (PFICA) with initializing by a simple color space transformation and a novel masking approach to automatically detect buildings from high resolution Google Earth imagery. ICA and FastICA algorithms are defined as Blind Source Separation (BSS) techniques for unmixing source signals using the reference data sets. In order to overcome the limitations of the ICA and FastICA algorithms and make them purposeful, we developed a novel method involving three main steps: 1-Improving the FastICA algorithm using Moore-Penrose pseudo inverse matrix model, 2-Automated seeding of the PFICA algorithm based on LUV color space and proposed simple rules to split image into three regions; shadow + vegetation, baresoil + roads and buildings, respectively, 3-Masking out the final building detection results from PFICA outputs utilizing the K-means clustering algorithm with two number of clusters and conducting simple morphological operations to remove noises. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.6% and 85.5% overall pixel-based and object-based precision performances, respectively.

Web GIS in practice X: a Microsoft Kinect natural user interface for Google Earth navigation

PubMed Central

2011-01-01

This paper covers the use of depth sensors such as Microsoft Kinect and ASUS Xtion to provide a natural user interface (NUI) for controlling 3-D (three-dimensional) virtual globes such as Google Earth (including its Street View mode), Bing Maps 3D, and NASA World Wind. The paper introduces the Microsoft Kinect device, briefly describing how it works (the underlying technology by PrimeSense), as well as its market uptake and application potential beyond its original intended purpose as a home entertainment and video game controller. The different software drivers available for connecting the Kinect device to a PC (Personal Computer) are also covered, and their comparative pros and cons briefly discussed. We survey a number of approaches and application examples for controlling 3-D virtual globes using the Kinect sensor, then describe Kinoogle, a Kinect interface for natural interaction with Google Earth, developed by students at Texas A&M University. Readers interested in trying out the application on their own hardware can download a Zip archive (included with the manuscript as additional files 1, 2, &3) that contains a 'Kinnogle installation package for Windows PCs'. Finally, we discuss some usability aspects of Kinoogle and similar NUIs for controlling 3-D virtual globes (including possible future improvements), and propose a number of unique, practical 'use scenarios' where such NUIs could prove useful in navigating a 3-D virtual globe, compared to conventional mouse/3-D mouse and keyboard-based interfaces. PMID:21791054

Web GIS in practice X: a Microsoft Kinect natural user interface for Google Earth navigation.

PubMed

Boulos, Maged N Kamel; Blanchard, Bryan J; Walker, Cory; Montero, Julio; Tripathy, Aalap; Gutierrez-Osuna, Ricardo

2011-07-26

This paper covers the use of depth sensors such as Microsoft Kinect and ASUS Xtion to provide a natural user interface (NUI) for controlling 3-D (three-dimensional) virtual globes such as Google Earth (including its Street View mode), Bing Maps 3D, and NASA World Wind. The paper introduces the Microsoft Kinect device, briefly describing how it works (the underlying technology by PrimeSense), as well as its market uptake and application potential beyond its original intended purpose as a home entertainment and video game controller. The different software drivers available for connecting the Kinect device to a PC (Personal Computer) are also covered, and their comparative pros and cons briefly discussed. We survey a number of approaches and application examples for controlling 3-D virtual globes using the Kinect sensor, then describe Kinoogle, a Kinect interface for natural interaction with Google Earth, developed by students at Texas A&M University. Readers interested in trying out the application on their own hardware can download a Zip archive (included with the manuscript as additional files 1, 2, &3) that contains a 'Kinnogle installation package for Windows PCs'. Finally, we discuss some usability aspects of Kinoogle and similar NUIs for controlling 3-D virtual globes (including possible future improvements), and propose a number of unique, practical 'use scenarios' where such NUIs could prove useful in navigating a 3-D virtual globe, compared to conventional mouse/3-D mouse and keyboard-based interfaces.

Vulnerability of the Nigerian coast: An insight into sea level rise owing to climate change and anthropogenic activities

NASA Astrophysics Data System (ADS)

Danladi, Iliya Bauchi; Kore, Basiru Mohammed; Gül, Murat

2017-10-01

Coastal areas are important regions in the world as they host huge population, diverse ecosystems and natural resources. However, owing to their settings, elevations and proximities to the sea, climate change (global warming) and human activities are threatening issues. Herein, we report the coastline changes and possible future threats related to sea level rise owing to global warming and human activities in the coastal region of Nigeria. Google earth images, Digital Elevation Model (DEM) and geological maps were used. Using google earth images, coastal changes for the past 43 years, 3 years prior to and after the construction of breakwaters along Goshen Beach Estate (Lekki) were examined. Additionally, coastline changes along Lekki Phase I from 2013 to 2016 were evaluated. The DEM map was used to delineate 0-2 m, 2-5 m and 5-10 m asl which correspond to undifferentiated sands and gravels to clays on the geological map. The results of the google earth images revealed remarkable erosion along both Lekki and Lekki Phase I, with the destruction of a lagoon in Lekki Phase I. Based on the result of the DEM map and geology, elevations of 0-2 m, 2-5 m and 5-10 m asl were interpreted as highly risky, moderately risky and risky respectively. Considering factors threatening coastal regions, the erosion and destruction of the lagoon along the Nigerian coast may be ascribed to sea level rise as a result of global warming and intense human activities respectively.

Measuring the Carolina Bays Using Archetype Template Overlays on the Google Earth Virtual Globe; Planform Metrics for 25,000 Bays Extracted from LiDAR and Satellite Imagery

NASA Astrophysics Data System (ADS)

Davias, M. E.; Gilbride, J. L.

2011-12-01

Aerial photographs of Carolina bays taken in the 1930's sparked the initial research into their geomorphology. Satellite Imagery available today through the Google Earth Virtual Globe facility expands the regions available for interrogation, but reveal only part of their unique planforms. Digital Elevation Maps (DEMs), using Light Detection And Ranging (LiDAR) remote sensing data, accentuate the visual presentation of these aligned ovoid shallow basins by emphasizing their robust circumpheral rims. To support a geospatial survey of Carolina bay landforms in the continental USA, 400,000 km2 of hsv-shaded DEMs were created as KML-JPEG tile sets. A majority of these DEMs were generated with LiDAR-derived data. We demonstrate the tile generation process and their integration into Google Earth, where the DEMs augment available photographic imagery for the visualization of bay planforms. While the generic Carolina bay planform is considered oval, we document subtle regional variations. Using a small set of empirically derived planform shapes, we created corresponding Google Earth overlay templates. We demonstrate the analysis of an individual Carolina bay by placing an appropriate overlay onto the virtually globe, then orientating, sizing and rotating it by edit handles such that it satisfactorily represents the bay's rim. The resulting overlay data element is extracted from Google Earth's object directory and programmatically processed to generate metrics such as geographic location, elevation, major and minor axis and inferred orientation. Utilizing a virtual globe facility for data capture may result in higher quality data compared to methods that reference flat maps, where geospatial shape and orientation of the bays could be skewed and distorted in the orthographic projection process. Using the methodology described, we have measured over 25k distinct Carolina bays. We discuss the Google Fusion geospatial data repository facility, through which these data have been assembled and made web-accessible to other researchers. Preliminary findings from the survey are discussed, such as how bay surface area, eccentricity and orientation vary across ~800 1/4° × 1/4° grid elements. Future work includes measuring 25k additional bays, as well as interrogation of the orientation data to identify any possible systematic geospatial relationships.

Visualization of seismic tomography on Google Earth: Improvement of KML generator and its web application to accept the data file in European standard format

NASA Astrophysics Data System (ADS)

Yamagishi, Y.; Yanaka, H.; Tsuboi, S.

2009-12-01

We have developed a conversion tool for the data of seismic tomography into KML, called KML generator, and made it available on the web site (http://www.jamstec.go.jp/pacific21/google_earth). The KML generator enables us to display vertical and horizontal cross sections of the model on Google Earth in three-dimensional manner, which would be useful to understand the Earth's interior. The previous generator accepts text files of grid-point data having longitude, latitude, and seismic velocity anomaly. Each data file contains the data for each depth. Metadata, such as bibliographic reference, grid-point interval, depth, are described in other information file. We did not allow users to upload their own tomographic model to the web application, because there is not standard format to represent tomographic model. Recently European seismology research project, NEIRES (Network of Research Infrastructures for European Seismology), advocates that the data of seismic tomography should be standardized. They propose a new format based on JSON (JavaScript Object Notation), which is one of the data-interchange formats, as a standard one for the tomography. This format consists of two parts, which are metadata and grid-point data values. The JSON format seems to be powerful to handle and to analyze the tomographic model, because the structure of the format is fully defined by JavaScript objects, thus the elements are directly accessible by a script. In addition, there exist JSON libraries for several programming languages. The International Federation of Digital Seismograph Network (FDSN) adapted this format as a FDSN standard format for seismic tomographic model. There might be a possibility that this format would not only be accepted by European seismologists but also be accepted as the world standard. Therefore we improve our KML generator for seismic tomography to accept the data file having also JSON format. We also improve the web application of the generator so that the JSON formatted data file can be uploaded. Users can convert any tomographic model data to KML. The KML obtained through the new generator should provide an arena to compare various tomographic models and other geophysical observations on Google Earth, which may act as a common platform for geoscience browser.

Crop classification and mapping based on Sentinel missions data in cloud environment

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.; Kussul, N.; Shelestov, A.; Vasiliev, V.

2017-12-01

Availability of high resolution satellite imagery (Sentinel-1/2/3, Landsat) over large territories opens new opportunities in agricultural monitoring. In particular, it becomes feasible to solve crop classification and crop mapping task at country and regional scale using time series of heterogenous satellite imagery. But in this case, we face with the problem of Big Data. Dealing with time series of high resolution (10 m) multispectral imagery we need to download huge volumes of data and then process them. The solution is to move "processing chain" closer to data itself to drastically shorten time for data transfer. One more advantage of such approach is the possibility to parallelize data processing workflow and efficiently implement machine learning algorithms. This could be done with cloud platform where Sentinel imagery are stored. In this study, we investigate usability and efficiency of two different cloud platforms Amazon and Google for crop classification and crop mapping problems. Two pilot areas were investigated - Ukraine and England. Google provides user friendly environment Google Earth Engine for Earth observation applications with a lot of data processing and machine learning tools already deployed. At the same time with Amazon one gets much more flexibility in implementation of his own workflow. Detailed analysis of pros and cons will be done in the presentation.

Known unknowns, Google Earth, plate tectonics and Mt Bellenden Ker: some thoughts on locality data.

PubMed

Mesibov, Robert

2012-01-01

Latitude/longitude data in locality records should be published with spatial uncertainties, datum(s) used and indications of how the data were obtained. Google Earth can be used to locate sampling sites, but the underlying georegistration of the satellite image should be checked. The little-known relabelling of a set of landmarks on Mt Bellenden Ker, a scientifically important collecting locality in tropical north Queensland, Australia, is documented as an example of the importance of checking records not accompanied by appropriately accurate latitude/longitude data.

Dynamic Server-Based KML Code Generator Method for Level-of-Detail Traversal of Geospatial Data

NASA Technical Reports Server (NTRS)

Baxes, Gregory; Mixon, Brian; Linger, TIm

2013-01-01

Web-based geospatial client applications such as Google Earth and NASA World Wind must listen to data requests, access appropriate stored data, and compile a data response to the requesting client application. This process occurs repeatedly to support multiple client requests and application instances. Newer Web-based geospatial clients also provide user-interactive functionality that is dependent on fast and efficient server responses. With massively large datasets, server-client interaction can become severely impeded because the server must determine the best way to assemble data to meet the client applications request. In client applications such as Google Earth, the user interactively wanders through the data using visually guided panning and zooming actions. With these actions, the client application is continually issuing data requests to the server without knowledge of the server s data structure or extraction/assembly paradigm. A method for efficiently controlling the networked access of a Web-based geospatial browser to server-based datasets in particular, massively sized datasets has been developed. The method specifically uses the Keyhole Markup Language (KML), an Open Geospatial Consortium (OGS) standard used by Google Earth and other KML-compliant geospatial client applications. The innovation is based on establishing a dynamic cascading KML strategy that is initiated by a KML launch file provided by a data server host to a Google Earth or similar KMLcompliant geospatial client application user. Upon execution, the launch KML code issues a request for image data covering an initial geographic region. The server responds with the requested data along with subsequent dynamically generated KML code that directs the client application to make follow-on requests for higher level of detail (LOD) imagery to replace the initial imagery as the user navigates into the dataset. The approach provides an efficient data traversal path and mechanism that can be flexibly established for any dataset regardless of size or other characteristics. The method yields significant improvements in userinteractive geospatial client and data server interaction and associated network bandwidth requirements. The innovation uses a C- or PHP-code-like grammar that provides a high degree of processing flexibility. A set of language lexer and parser elements is provided that offers a complete language grammar for writing and executing language directives. A script is wrapped and passed to the geospatial data server by a client application as a component of a standard KML-compliant statement. The approach provides an efficient means for a geospatial client application to request server preprocessing of data prior to client delivery. Data is structured in a quadtree format. As the user zooms into the dataset, geographic regions are subdivided into four child regions. Conversely, as the user zooms out, four child regions collapse into a single, lower-LOD region. The approach provides an efficient data traversal path and mechanism that can be flexibly established for any dataset regardless of size or other characteristics.

Public Use of Online Hydrology Information for Harris County and Houston, Texas, during Hurricane Harvey and Suggested Improvement for Future Flood Events

NASA Astrophysics Data System (ADS)

Lilly, M. R.; Feditova, A.; Levine, K.; Giardino, J. R.

2017-12-01

The Harris County Flood Control District has an impressive amount of information available for the public related to flood management and response. During Hurricane Harvey, this information was used by the authors to help address daily questions from family and friends living in the Houston area. Common near-real-time reporting data included precipitation and water levels. Maps included locations of data stations, stream or bayou conditions (in bank, out of bank) and watershed or drainage boundaries. In general, the data station reporting and online information was updating well throughout the hurricane and post-flooding period. Only a few of the data reporting stations had problems with water level sensor measurements. The overall information was helpful to hydrologists and floodplain managers. The online information could not easily answer all common questions residents may have during a flood event. Some of the more common questions were how to use the water-level information to know the potential extent of flooding and relative location of flooding to the location of residents. To help address the questions raised during the flooding on how to use the available water level data, we used Google Earth to get lot and intersection locations to help show the relative differences between nearby water-level stations and residences of interest. The reported resolution of the Google Earth elevation data is 1-foot. To help confirm the use of this data, we compared Google Earth approximate elevations with reported Harris County Floodplain Reference Mark individual reports. This method helped verify we could use the Google Earth information for approximate comparisons. We also faced questions on what routes to take if evacuation was needed, and where to go to get to higher ground elevations. Google Earth again provided a helpful and easy to use interface to look at road and intersection elevations and develop suggested routes for family and friends to take to avoid low areas that may be subject to flooding. These and other recommendations that helped answer common questions by residents reacting to the hurricane and subsequent flooding conditions are summarized with examples.

Development, Deployment, and Assessment of Dynamic Geological and Geophysical Models Using the Google Earth APP and API: Implications for Undergraduate Education in the Earth and Planetary Sciences

NASA Astrophysics Data System (ADS)

de Paor, D. G.; Whitmeyer, S. J.; Gobert, J.

2009-12-01

We previously reported on innovative techniques for presenting data on virtual globes such as Google Earth using emergent Collada models that reveal subsurface geology and geophysics. We here present several new and enhanced models and linked lesson plans to aid deployment in undergraduate geoscience courses, along with preliminary results from our assessment of their effectiveness. The new Collada models are created with Google SketchUp, Bonzai3D, and MeshLab software, and are grouped to cover (i) small scale field mapping areas; (ii) regional scale studies of the North Atlantic Ocean Basin, the Appalachian Orogen, and the Pacific Ring of Fire; and (iii) global scale studies of terrestrial planets, moons, and asteroids. Enhancements include emergent block models with three-dimensional surface topography; models that conserve structural orientation data; interactive virtual specimens; models that animate plate movements on the virtual globe; exploded 3-D views of planetary mantles and cores; and server-generated dynamic KML. We tested volunteer students and professors using Silverback monitoring software, think-aloud verbalizations, and questionnaires designed to assess their understanding of the underlying geo-scientific phenomena. With the aid of a cohort of instructors across the U.S., we are continuing to assess areas in which users encounter difficulties with both the software and geoscientific concepts. Preliminary results suggest that it is easy to overestimate the computer expertise of novice users even when they are content knowledge experts (i.e., instructors), and that a detailed introduction to virtual globe manipulation is essential before moving on to geoscience applications. Tasks that seem trivial to developers may present barriers to non-technical users and technicalities that challenge instructors may block adoption in the classroom. We have developed new models using the Google Earth API which permits enhanced interaction and dynamic feedback and are assessing their relative merits versus the Google Earth APP. Overall, test students and professors value the models very highly. There are clear pedagogical opportunities for using materials such as these to create engaging in-course research opportunities for undergraduates.

Content and Language Integrated Learning through an Online Game in Primary School: A Case Study

ERIC Educational Resources Information Center

Dourda, Kyriaki; Bratitsis, Tharrenos; Griva, Eleni; Papadopoulou, Penelope

2014-01-01

In this paper an educational design proposal is presented which combines two well established teaching approaches, that of Game-based Learning (GBL) and Content and Language Integrated Learning (CLIL). The context of the proposal was the design of an educational geography computer game, utilizing QR Codes and Google Earth for teaching English…

Using Google Earth and Satellite Imagery to Foster Place-Based Teaching in an Introductory Physical Geology Course

ERIC Educational Resources Information Center

Monet, Julie; Greene, Todd

2012-01-01

Students in an introductory physical geology course often have difficulty making connections between basic course topics and assembling key concepts (beyond textbook examples) to interpret how geologic processes shape the characteristics of the local and regional natural environment. As an approach to address these issues, we designed and…

Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries

PubMed Central

Chang, Aileen Y; Parrales, Maria E; Jimenez, Javier; Sobieszczyk, Magdalena E; Hammer, Scott M; Copenhaver, David J; Kulkarni, Rajan P

2009-01-01

Background Dengue fever is a mosquito-borne illness that places significant burden on tropical developing countries with unplanned urbanization. A surveillance system using Google Earth and GIS mapping technologies was developed in Nicaragua as a management tool. Methods and Results Satellite imagery of the town of Bluefields, Nicaragua captured from Google Earth was used to create a base-map in ArcGIS 9. Indices of larval infestation, locations of tire dumps, cemeteries, large areas of standing water, etc. that may act as larval development sites, and locations of the homes of dengue cases collected during routine epidemiologic surveying were overlaid onto this map. Visual imagery of the location of dengue cases, larval infestation, and locations of potential larval development sites were used by dengue control specialists to prioritize specific neighborhoods for targeted control interventions. Conclusion This dengue surveillance program allows public health workers in resource-limited settings to accurately identify areas with high indices of mosquito infestation and interpret the spatial relationship of these areas with potential larval development sites such as garbage piles and large pools of standing water. As a result, it is possible to prioritize control strategies and to target interventions to highest risk areas in order to eliminate the likely origin of the mosquito vector. This program is well-suited for resource-limited settings since it utilizes readily available technologies that do not rely on Internet access for daily use and can easily be implemented in many developing countries for very little cost. PMID:19627614

BErkeley Atmospheric CO2 Network (BEACON) - Bringing Measurements of CO2 Emissions to a School Near You

NASA Astrophysics Data System (ADS)

Teige, V. E.; Havel, E.; Patt, C.; Heber, E.; Cohen, R. C.

2011-12-01

The University of California at Berkeley in collaboration with the Chabot Space and Science Center describe a set of educational programs, workshops, and exhibits based on a multi-node greenhouse gas and air quality monitoring network being deployed over Oakland, California. Examining raw numerical data using highly engaging and effective geo-data visualization tools like Google Earth can make the science come alive for students, and provide a hook for drawing them into deeper investigations. The Climate Science Investigations teacher workshop at the Chabot Space and Science Center will make use of Google Earth, Excel, and other geo-data visualization tools to step students through the process from data acquisition to discovery. Using multiple data sources, including output from the BErkeley Atmospheric CO2 Network (BEACON) project, participants will be encouraged to explore a variety of different modes of data display toward producing a unique, and ideally insightful, illumination of the data.

Using Google Earth to Assess Shade for Sun Protection in Urban Recreation Spaces: Methods and Results.

PubMed

Gage, R; Wilson, N; Signal, L; Barr, M; Mackay, C; Reeder, A; Thomson, G

2018-05-16

Shade in public spaces can lower the risk of and sun burning and skin cancer. However, existing methods of auditing shade require travel between sites, and sunny weather conditions. This study aimed to evaluate the feasibility of free computer software-Google Earth-for assessing shade in urban open spaces. A shade projection method was developed that uses Google Earth street view and aerial images to estimate shade at solar noon on the summer solstice, irrespective of the date of image capture. Three researchers used the method to separately estimate shade cover over pre-defined activity areas in a sample of 45 New Zealand urban open spaces, including 24 playgrounds, 12 beaches and 9 outdoor pools. Outcome measures included method accuracy (assessed by comparison with a subsample of field observations of 10 of the settings) and inter-rater reliability. Of the 164 activity areas identified in the 45 settings, most (83%) had no shade cover. The method identified most activity areas in playgrounds (85%) and beaches (93%) and was accurate for assessing shade over these areas (predictive values of 100%). Only 8% of activity areas at outdoor pools were identified, due to a lack of street view images. Reliability for shade cover estimates was excellent (intraclass correlation coefficient of 0.97, 95% CI 0.97-0.98). Google Earth appears to be a reasonably accurate and reliable and shade audit tool for playgrounds and beaches. The findings are relevant for programmes focused on supporting the development of healthy urban open spaces.

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

NASA Astrophysics Data System (ADS)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

GeolOkit 1.0: a new Open Source, Cross-Platform software for geological data visualization in Google Earth environment

NASA Astrophysics Data System (ADS)

Triantafyllou, Antoine; Bastin, Christophe; Watlet, Arnaud

2016-04-01

GIS software suites are today's essential tools to gather and visualise geological data, to apply spatial and temporal analysis and in fine, to create and share interactive maps for further geosciences' investigations. For these purposes, we developed GeolOkit: an open-source, freeware and lightweight software, written in Python, a high-level, cross-platform programming language. GeolOkit software is accessible through a graphical user interface, designed to run in parallel with Google Earth. It is a super user-friendly toolbox that allows 'geo-users' to import their raw data (e.g. GPS, sample locations, structural data, field pictures, maps), to use fast data analysis tools and to plot these one into Google Earth environment using KML code. This workflow requires no need of any third party software, except Google Earth itself. GeolOkit comes with large number of geosciences' labels, symbols, colours and placemarks and may process : (i) multi-points data, (ii) contours via several interpolations methods, (iii) discrete planar and linear structural data in 2D or 3D supporting large range of structures input format, (iv) clustered stereonets and rose diagram, (v) drawn cross-sections as vertical sections, (vi) georeferenced maps and vectors, (vii) field pictures using either geo-tracking metadata from a camera built-in GPS module, or the same-day track of an external GPS. We are looking for you to discover all the functionalities of GeolOkit software. As this project is under development, we are definitely looking to discussions regarding your proper needs, your ideas and contributions to GeolOkit project.

A two-stage cluster sampling method using gridded population data, a GIS, and Google Earth(TM) imagery in a population-based mortality survey in Iraq.

PubMed

Galway, Lp; Bell, Nathaniel; Sae, Al Shatari; Hagopian, Amy; Burnham, Gilbert; Flaxman, Abraham; Weiss, Wiliam M; Rajaratnam, Julie; Takaro, Tim K

2012-04-27

Mortality estimates can measure and monitor the impacts of conflict on a population, guide humanitarian efforts, and help to better understand the public health impacts of conflict. Vital statistics registration and surveillance systems are rarely functional in conflict settings, posing a challenge of estimating mortality using retrospective population-based surveys. We present a two-stage cluster sampling method for application in population-based mortality surveys. The sampling method utilizes gridded population data and a geographic information system (GIS) to select clusters in the first sampling stage and Google Earth TM imagery and sampling grids to select households in the second sampling stage. The sampling method is implemented in a household mortality study in Iraq in 2011. Factors affecting feasibility and methodological quality are described. Sampling is a challenge in retrospective population-based mortality studies and alternatives that improve on the conventional approaches are needed. The sampling strategy presented here was designed to generate a representative sample of the Iraqi population while reducing the potential for bias and considering the context specific challenges of the study setting. This sampling strategy, or variations on it, are adaptable and should be considered and tested in other conflict settings.

A two-stage cluster sampling method using gridded population data, a GIS, and Google EarthTM imagery in a population-based mortality survey in Iraq

PubMed Central

2012-01-01

Background Mortality estimates can measure and monitor the impacts of conflict on a population, guide humanitarian efforts, and help to better understand the public health impacts of conflict. Vital statistics registration and surveillance systems are rarely functional in conflict settings, posing a challenge of estimating mortality using retrospective population-based surveys. Results We present a two-stage cluster sampling method for application in population-based mortality surveys. The sampling method utilizes gridded population data and a geographic information system (GIS) to select clusters in the first sampling stage and Google Earth TM imagery and sampling grids to select households in the second sampling stage. The sampling method is implemented in a household mortality study in Iraq in 2011. Factors affecting feasibility and methodological quality are described. Conclusion Sampling is a challenge in retrospective population-based mortality studies and alternatives that improve on the conventional approaches are needed. The sampling strategy presented here was designed to generate a representative sample of the Iraqi population while reducing the potential for bias and considering the context specific challenges of the study setting. This sampling strategy, or variations on it, are adaptable and should be considered and tested in other conflict settings. PMID:22540266

Development of a Carbon Sequestration Visualization Tool using Google Earth Pro

NASA Astrophysics Data System (ADS)

Keating, G. N.; Greene, M. K.

2008-12-01

The Big Sky Carbon Sequestration Partnership seeks to prepare organizations throughout the western United States for a possible carbon-constrained economy. Through the development of CO2 capture and subsurface sequestration technology, the Partnership is working to enable the region to cleanly utilize its abundant fossil energy resources. The intent of the Los Alamos National Laboratory Big Sky Visualization tool is to allow geochemists, geologists, geophysicists, project managers, and other project members to view, identify, and query the data collected from CO2 injection tests using a single data source platform, a mission to which Google Earth Pro is uniquely and ideally suited . The visualization framework enables fusion of data from disparate sources and allows investigators to fully explore spatial and temporal trends in CO2 fate and transport within a reservoir. 3-D subsurface wells are projected above ground in Google Earth as the KML anchor points for the presentation of various surface subsurface data. This solution is the most integrative and cost-effective possible for the variety of users in the Big Sky community.

A Web Portal-Based Time-Aware KML Animation Tool for Exploring Spatiotemporal Dynamics of Hydrological Events

NASA Astrophysics Data System (ADS)

Bao, X.; Cai, X.; Liu, Y.

2009-12-01

Understanding spatiotemporal dynamics of hydrological events such as storms and droughts is highly valuable for decision making on disaster mitigation and recovery. Virtual Globe-based technologies such as Google Earth and Open Geospatial Consortium KML standards show great promises for collaborative exploration of such events using visual analytical approaches. However, currently there are two barriers for wider usage of such approaches. First, there lacks an easy way to use open source tools to convert legacy or existing data formats such as shapefiles, geotiff, or web services-based data sources to KML and to produce time-aware KML files. Second, an integrated web portal-based time-aware animation tool is currently not available. Thus users usually share their files in the portal but have no means to visually explore them without leaving the portal environment which the users are familiar with. We develop a web portal-based time-aware KML animation tool for viewing extreme hydrologic events. The tool is based on Google Earth JavaScript API and Java Portlet standard 2.0 JSR-286, and it is currently deployable in one of the most popular open source portal frameworks, namely Liferay. We have also developed an open source toolkit kml-soc-ncsa (http://code.google.com/p/kml-soc-ncsa/) to facilitate the conversion of multiple formats into KML and the creation of time-aware KML files. We illustrate our tool using some example cases, in which drought and storm events with both time and space dimension can be explored in this web-based KML animation portlet. The tool provides an easy-to-use web browser-based portal environment for multiple users to collaboratively share and explore their time-aware KML files as well as improving the understanding of the spatiotemporal dynamics of the hydrological events.

Generating and Visualizing Climate Indices using Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Guentchev, G.; Rood, R. B.

2017-12-01

Climate change is expected to have largest impacts on regional and local scales. Relevant and credible climate information is needed to support the planning and adaptation efforts in our communities. The volume of climate projections of temperature and precipitation is steadily increasing, as datasets are being generated on finer spatial and temporal grids with an increasing number of ensembles to characterize uncertainty. Despite advancements in tools for querying and retrieving subsets of these large, multi-dimensional datasets, ease of access remains a barrier for many existing and potential users who want to derive useful information from these data, particularly for those outside of the climate modelling research community. Climate indices, that can be derived from daily temperature and precipitation data, such as annual number of frost days or growing season length, can provide useful information to practitioners and stakeholders. For this work the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was loaded into Google Earth Engine, a cloud-based geospatial processing platform. Algorithms that use the Earth Engine API to generate several climate indices were written. The indices were chosen from the set developed by the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). Simple user interfaces were created that allow users to query, produce maps and graphs of the indices, as well as download results for additional analyses. These browser-based interfaces could allow users in low-bandwidth environments to access climate information. This research shows that calculating climate indices from global downscaled climate projection datasets and sharing them widely using cloud computing technologies is feasible. Further development will focus on exposing the climate indices to existing applications via the Earth Engine API, and building custom user interfaces for presenting climate indices to a diverse set of user groups.

International Ocean Discovery Program U.S. Implementing Organization

Science.gov Websites

coordinates seagoing expeditions to study the history of the Earth recorded in sediments and rocks beneath the Internship :: Minorities in Scientific Ocean Drilling Fellowship Education Deep Earth Academy logo :: joidesresolution.org :: For students :: For teachers :: For scientists :: View drill sites in Google Earth Export

Developing a Global Database of Historic Flood Events to Support Machine Learning Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Sullivan, J.; Kettner, A.; Brakenridge, G. R.; Slayback, D. A.; Kuhn, C.; Doyle, C.

2016-12-01

There is an increasing need to understand flood vulnerability as the societal and economic effects of flooding increases. Risk models from insurance companies and flood models from hydrologists must be calibrated based on flood observations in order to make future predictions that can improve planning and help societies reduce future disasters. Specifically, to improve these models both traditional methods of flood prediction from physically based models as well as data-driven techniques, such as machine learning, require spatial flood observation to validate model outputs and quantify uncertainty. A key dataset that is missing for flood model validation is a global historical geo-database of flood event extents. Currently, the most advanced database of historical flood extent is hosted and maintained at the Dartmouth Flood Observatory (DFO) that has catalogued 4320 floods (1985-2015) but has only mapped 5% of these floods. We are addressing this data gap by mapping the inventory of floods in the DFO database to create a first-of- its-kind, comprehensive, global and historical geospatial database of flood events. To do so, we combine water detection algorithms on MODIS and Landsat 5,7 and 8 imagery in Google Earth Engine to map discrete flood events. The created database will be available in the Earth Engine Catalogue for download by country, region, or time period. This dataset can be leveraged for new data-driven hydrologic modeling using machine learning algorithms in Earth Engine's highly parallelized computing environment, and we will show examples for New York and Senegal.

GeoDash: Assisting Visual Image Interpretation in Collect Earth Online by Leveraging Big Data on Google Earth Engine

NASA Technical Reports Server (NTRS)

Markert, Kel; Ashmall, William; Johnson, Gary; Saah, David; Mollicone, Danilo; Diaz, Alfonso Sanchez-Paus; Anderson, Eric; Flores, Africa; Griffin, Robert

2017-01-01

Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.

GeoDash: Assisting Visual Image Interpretation in Collect Earth Online by Leveraging Big Data on Google Earth Engine

NASA Astrophysics Data System (ADS)

Markert, K. N.; Ashmall, W.; Johnson, G.; Saah, D. S.; Anderson, E.; Flores Cordova, A. I.; Díaz, A. S. P.; Mollicone, D.; Griffin, R.

2017-12-01

Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.

Integrating Socioeconomic and Earth Science Data Using Geobrowsers and Web Services: A Demonstration

NASA Astrophysics Data System (ADS)

Schumacher, J. A.; Yetman, G. G.

2007-12-01

The societal benefit areas identified as the focus for the Global Earth Observing System of Systems (GEOSS) 10- year implementation plan are an indicator of the importance of integrating socioeconomic data with earth science data to support decision makers. To aid this integration, CIESIN is delivering its global and U.S. demographic data to commercial and open source Geobrowsers and providing open standards based services for data access. Currently, data on population distribution, poverty, and detailed census data for the U.S. are available for visualization and access in Google Earth, NASA World Wind, and a browser-based 2-dimensional mapping client. The mapping client allows for the creation of web map documents that pull together layers from distributed servers and can be saved and shared. Visualization tools with Geobrowsers, user-driven map creation and sharing via browser-based clients, and a prototype for characterizing populations at risk to predicted precipitation deficits will be demonstrated.

Development of a Web-Based Visualization Platform for Climate Research Using Google Earth

NASA Technical Reports Server (NTRS)

Sun, Xiaojuan; Shen, Suhung; Leptoukh, Gregory G.; Wang, Panxing; Di, Liping; Lu, Mingyue

2011-01-01

Recently, it has become easier to access climate data from satellites, ground measurements, and models from various data centers, However, searching. accessing, and prc(essing heterogeneous data from different sources are very tim -consuming tasks. There is lack of a comprehensive visual platform to acquire distributed and heterogeneous scientific data and to render processed images from a single accessing point for climate studies. This paper. documents the design and implementation of a Web-based visual, interoperable, and scalable platform that is able to access climatological fields from models, satellites, and ground stations from a number of data sources using Google Earth (GE) as a common graphical interface. The development is based on the TCP/IP protocol and various data sharing open sources, such as OPeNDAP, GDS, Web Processing Service (WPS), and Web Mapping Service (WMS). The visualization capability of integrating various measurements into cE extends dramatically the awareness and visibility of scientific results. Using embedded geographic information in the GE, the designed system improves our understanding of the relationships of different elements in a four dimensional domain. The system enables easy and convenient synergistic research on a virtual platform for professionals and the general public, gr$tly advancing global data sharing and scientific research collaboration.

Geolokit: An interactive tool for visualising and exploring geoscientific data in Google Earth

NASA Astrophysics Data System (ADS)

Triantafyllou, Antoine; Watlet, Arnaud; Bastin, Christophe

2017-10-01

Virtual globes have been developed to showcase different types of data combining a digital elevation model and basemaps of high resolution satellite imagery. Hence, they became a standard to share spatial data and information, although they suffer from a lack of toolboxes dedicated to the formatting of large geoscientific dataset. From this perspective, we developed Geolokit: a free and lightweight software that allows geoscientists - and every scientist working with spatial data - to import their data (e.g., sample collections, structural geology, cross-sections, field pictures, georeferenced maps), to handle and to transcribe them to Keyhole Markup Language (KML) files. KML files are then automatically opened in the Google Earth virtual globe and the spatial data accessed and shared. Geolokit comes with a large number of dedicated tools that can process and display: (i) multi-points data, (ii) scattered data interpolations, (iii) structural geology features in 2D and 3D, (iv) rose diagrams, stereonets and dip-plunge polar histograms, (v) cross-sections and oriented rasters, (vi) georeferenced field pictures, (vii) georeferenced maps and projected gridding. Therefore, together with Geolokit, Google Earth becomes not only a powerful georeferenced data viewer but also a stand-alone work platform. The toolbox (available online at http://www.geolokit.org) is written in Python, a high-level, cross-platform programming language and is accessible through a graphical user interface, designed to run in parallel with Google Earth, through a workflow that requires no additional third party software. Geolokit features are demonstrated in this paper using typical datasets gathered from two case studies illustrating its applicability at multiple scales of investigation: a petro-structural investigation of the Ile d'Yeu orthogneissic unit (Western France) and data collection of the Mariana oceanic subduction zone (Western Pacific).

Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine

NASA Astrophysics Data System (ADS)

Farda, N. M.

2017-12-01

Coastal wetlands provide ecosystem services essential to people and the environment. Changes in coastal wetlands, especially on land use, are important to monitor by utilizing multi-temporal imagery. The Google Earth Engine (GEE) provides many machine learning algorithms (10 algorithms) that are very useful for extracting land use from imagery. The research objective is to explore machine learning in Google Earth Engine and its accuracy for multi-temporal land use mapping of coastal wetland area. Landsat 3 MSS (1978), Landsat 5 TM (1991), Landsat 7 ETM+ (2001), and Landsat 8 OLI (2014) images located in Segara Anakan lagoon are selected to represent multi temporal images. The input for machine learning are visible and near infrared bands, PCA band, invers PCA bands, bare soil index, vegetation index, wetness index, elevation from ASTER GDEM, and GLCM (Harralick) texture, and also polygon samples in 140 locations. There are 10 machine learning algorithms applied to extract coastal wetlands land use from Landsat imagery. The algorithms are Fast Naive Bayes, CART (Classification and Regression Tree), Random Forests, GMO Max Entropy, Perceptron (Multi Class Perceptron), Winnow, Voting SVM, Margin SVM, Pegasos (Primal Estimated sub-GrAdient SOlver for Svm), IKPamir (Intersection Kernel Passive Aggressive Method for Information Retrieval, SVM). Machine learning in Google Earth Engine are very helpful in multi-temporal land use mapping, the highest accuracy for land use mapping of coastal wetland is CART with 96.98 % Overall Accuracy using K-Fold Cross Validation (K = 10). GEE is particularly useful for multi-temporal land use mapping with ready used image and classification algorithms, and also very challenging for other applications.

Wallace Creek Virtual Field Trip: Teaching Geoscience Concepts with LiDAR

NASA Astrophysics Data System (ADS)

Robinson, S. E.; Arrowsmith, R.; Crosby, C. J.

2009-12-01

Recently available data such as LiDAR (Light Detection and Ranging) high-resolution topography can assist students to better visualize and understand geosciences concepts. It is important to bring these data into geosciences curricula as teaching aids while ensuring that the visualization tools, virtual environments, etc. do not serve as barriers to student learning. As a Southern California Earthquake Center ACCESS-G intern, I am creating a “virtual field trip” to Wallace Creek along the San Andreas Fault (SAF) using Google Earth as a platform and the B4 project LiDAR data. Wallace Creek is an excellent site for understanding the centennial-to-millennial record of SAF slip because of its dramatic stream offsets. Using the LiDAR data instead of, or alongside, traditional visualizations and teaching methods enhances a student’s ability to understand plate tectonics, the earthquake cycle, strike-slip faults, and geomorphology. Viewing a high-resolution representation of the topography in Google Earth allows students to analyze the landscape and answer questions about the behavior of the San Andreas Fault. The activity guides students along the fault allowing them to measure channel offsets using the Google Earth measuring tool. Knowing the ages of channels, they calculate slip rate. They look for the smallest channel offsets around Wallace Creek in order to determine the slip per event. At both a “LiDAR and Education” workshop and the Cyberinfrastructure Summer Institute for Geoscientists (CSIG), I presented the Wallace Creek activity to high school and college earth science teachers. The teachers were positive in their responses and had numerous important suggestions including the need for a teacher’s manual for instruction and scientific background, and that the student goals and science topics should be specific and well-articulated for the sake of both the teacher and the student. The teachers also noted that the technology in classrooms varies significantly. Some do not have computers available for students or do not have access to the internet or certain software licenses. For this reason, I am also creating a paper-based version of the same exercise. After a usable activity is developed I plan to make it available online through the OpenTopography portal (www.opentopography.com) using a format similar to the online teaching boxes seen at DLESE (www.dlese.org). The final version will facilitate visual student learning through the popular Google Earth platform along with student guides and a teacher’s manual.

The 2nd Generation Real Time Mission Monitor (RTMM) Development

NASA Technical Reports Server (NTRS)

Blakeslee, Richard; Goodman, Michael; Meyer, Paul; Hardin, Danny; Hall, John; He, Yubin; Regner, Kathryn; Conover, Helen; Smith, Tammy; Lu, Jessica;

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

NASA Radar Images Show Continued Deformation from Mexico Quake

NASA Image and Video Library

2010-08-04

This image shows a UAVSAR interferogram swath overlaid atop a Google Earth image. New NASA airborne radar images show the continuing deformation in Earth surface resulting from the magnitude 7.2 temblor in Baja California on April 4, 2010.

Monitoring Urban Heat Island Through Google Earth Engine: Potentialities and Difficulties in Different Cities of the United States

NASA Astrophysics Data System (ADS)

Ravanelli, R.; Nascetti, A.; Cirigliano, R. V.; Di Rico, C.; Monti, P.; Crespi, M.

2018-04-01

The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google Earth Engine platform in order to investigate the temporal variations of the Urban Heat Island phenomenon as a whole. A intuitive methodology implementing a largescale correlation analysis between the Land Surface Temperature and Land Cover alterations was thus developed.The results obtained for the Phoenix MA are promising and show how the urbanization heavily affects the magnitude of the UHI effects with significant increases in LST. The proposed methodology is therefore able to efficiently monitor the UHI phenomenon.

Changes of Earthquake Vulnerability of Marunouchi and Ginza Area in Tokyo and Urban Recovery Digital Archives on Google Earth

NASA Astrophysics Data System (ADS)

Igarashi, Masayasu; Murao, Osamu

In this paper, the authors develop a multiple regression model which estimates urban earthquake vulnerability (building collapse risk and conflagration risk) for different eras, and clarify the historical changes of urban risk in Marunouchi and Ginza Districts in Tokyo, Japan using old maps and contemporary geographic information data. Also, we compare the change of urban vulnerability of the districts with the significant historical events in Tokyo. Finally, the results are loaded onto Google Earth with timescale extension to consider the possibility of urban recovery digital archives in the era of the recent geoinformatic technologies.

3D Visualization of near real-time remote-sensing observation for hurricanes field campaign using Google Earth API

NASA Astrophysics Data System (ADS)

Li, P.; Turk, J.; Vu, Q.; Knosp, B.; Hristova-Veleva, S. M.; Lambrigtsen, B.; Poulsen, W. L.; Licata, S.

2009-12-01

NASA is planning a new field experiment, the Genesis and Rapid Intensification Processes (GRIP), in the summer of 2010 to better understand how tropical storms form and develop into major hurricanes. The DC-8 aircraft and the Global Hawk Unmanned Airborne System (UAS) will be deployed loaded with instruments for measurements including lightning, temperature, 3D wind, precipitation, liquid and ice water contents, aerosol and cloud profiles. During the field campaign, both the spaceborne and the airborne observations will be collected in real-time and integrated with the hurricane forecast models. This observation-model integration will help the campaign achieve its science goals by allowing team members to effectively plan the mission with current forecasts. To support the GRIP experiment, JPL developed a website for interactive visualization of all related remote-sensing observations in the GRIP’s geographical domain using the new Google Earth API. All the observations are collected in near real-time (NRT) with 2 to 5 hour latency. The observations include a 1KM blended Sea Surface Temperature (SST) map from GHRSST L2P products; 6-hour composite images of GOES IR; stability indices, temperature and vapor profiles from AIRS and AMSU-B; microwave brightness temperature and rain index maps from AMSR-E, SSMI and TRMM-TMI; ocean surface wind vectors, vorticity and divergence of the wind from QuikSCAT; the 3D precipitation structure from TRMM-PR and vertical profiles of cloud and precipitation from CloudSAT. All the NRT observations are collected from the data centers and science facilities at NASA and NOAA, subsetted, re-projected, and composited into hourly or daily data products depending on the frequency of the observation. The data products are then displayed on the 3D Google Earth plug-in at the JPL Tropical Cyclone Information System (TCIS) website. The data products offered by the TCIS in the Google Earth display include image overlays, wind vectors, clickable placemarks with vertical profiles for temperature and water vapors and curtain plots along the satellite tracks. Multiple products can be overlaid with individual adjustable opacity control. The time sequence visualization is supported by calendar and Google Earth time animation. The work described here was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Cross-disciplinary Undergraduate Research: A Case Study in Digital Mapping, western Ireland

NASA Astrophysics Data System (ADS)

Whitmeyer, S. J.; de Paor, D. G.; Nicoletti, J.; Rivera, M.; Santangelo, B.; Daniels, J.

2008-12-01

As digital mapping technology becomes ever more advanced, field geologists spend a greater proportion of time learning digital methods relative to analyzing rocks and structures. To explore potential solutions to the time commitment implicit in learning digital field methods, we paired James Madison University (JMU) geology majors (experienced in traditional field techniques) with Worcester Polytechnic Institute (WPI) engineering students (experienced in computer applications) during a four week summer mapping project in Connemara, western Ireland. The project consisted of approximately equal parts digital field mapping (directed by the geology students), and lab-based map assembly, evaluation and formatting for virtual 3D terrains (directed by the engineering students). Students collected geologic data in the field using ruggedized handheld computers (Trimble GeoExplorer® series) with ArcPAD® software. Lab work initially focused on building geologic maps in ArcGIS® from the digital field data and then progressed to developing Google Earth-based visualizations of field data and maps. Challenges included exporting GIS data, such as locations and attributes, to KML tags for viewing in Google Earth, which we accomplished using a Linux bash script written by one of our engineers - a task outside the comfort zone of the average geology major. We also attempted to expand the scope of Google Earth by using DEMs of present-day geologically-induced landforms as representative models for paleo-geographic reconstructions of the western Ireland field area. As our integrated approach to digital field work progressed, we found that our digital field mapping produced data at a faster rate than could be effectively managed during our allotted time for lab work. This likely reflected the more developed methodology for digital field data collection, as compared with our lab-based attempts to develop new methods for 3D visualization of geologic maps. However, this experiment in cross-disciplinary undergraduate research was a big success, with an enthusiastic interchange of expertise between undergraduate geology and engineering students that produced new, cutting-edge methods for visualizing geologic data and maps.

Integrating Authentic Earth Science Data in Online Visualization Tools and Social Media Networking to Promote Earth Science Education

NASA Astrophysics Data System (ADS)

Carter, B. L.; Campbell, B.; Chambers, L.; Davis, A.; Riebeek, H.; Ward, K.

2008-12-01

The Goddard Space Flight Center (GSFC) is one of the largest Earth Science research-based institutions in the nation. Along with the research comes a dedicated group of people who are tasked with developing Earth science research-based education and public outreach materials to reach the broadest possible range of audiences. The GSFC Earth science education community makes use of a wide variety of platforms in order to reach their goals of communicating science. These platforms include using social media networking such as Twitter and Facebook, as well as geo-spatial tools such as MY NASA DATA, NASA World Wind, NEO, and Google Earth. Using a wide variety of platforms serves the dual purposes of promoting NASA Earth Science research and making authentic data available to educational communities that otherwise might not otherwise be granted access. Making data available to education communities promotes scientific literacy through the investigation of scientific phenomena using the same data that is used by the scientific community. Data from several NASA missions will be used to demonstrate the ways in which Earth science data are made available for the education community.

Multi-Instrument Tools and Services to Access NASA Earth Science Data from the GSFC Earth Sciences Data and Information Services Center

NASA Technical Reports Server (NTRS)

Kempler, Steve; Leptoukh, Greg; Lynnes, Chris

2010-01-01

The presentation purpose is to describe multi-instrument tools and services that facilitate access and usability of NASA Earth science data at Goddard Space Flight Center (GSFC). NASA's Earth observing system includes 14 satellites. Topics include EOSDIS facilities and system architecture, and overview of GSFC Earth Science Data and Information Services Center (GES DISC) mission, Mirador data search, Giovanni, multi-instrument data exploration, Google Earth[TM], data merging, and applications.

Mapping rice extent map with crop intensity in south China through integration of optical and microwave images based on google earth engine

NASA Astrophysics Data System (ADS)

Zhang, X.; Wu, B.; Zhang, M.; Zeng, H.

2017-12-01

Rice is one of the main staple foods in East Asia and Southeast Asia, which has occupied more than half of the world's population with 11% of cultivated land. Study on rice can provide direct or indirect information on food security and water source management. Remote sensing has proven to be the most effective method to monitoring the cropland in large scale by using temporary and spectral information. There are two main kinds of satellite have been used to mapping rice including microwave and optical. Rice, as the main crop of paddy fields, the main feature different from other crops is flooding phenomenon at planning stage (Figure 1). Microwave satellites can penetrate through clouds and efficiency on monitoring flooding phenomenon. Meanwhile, the vegetation index based on optical satellite can well distinguish rice from other vegetation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. Google has collected large number of remote sensing satellite data around the world, which providing researchers with the possibility of doing application by using multi-source remote sensing data in a large area. In this work, we map rice planting area in south China through integration of Landsat-8 OLI, Sentienl-2, and Sentinel-1 Synthetic Aperture Radar (SAR) images. The flowchart is shown in figure 2. First, a threshold method the VH polarized backscatter from SAR sensor and vegetation index including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from optical sensor were used the classify the rice extent map. The forest and water surface extent map provided by earth engine were used to mask forest and water. To overcome the problem of the "salt and pepper effect" by Pixel-based classification when the spatial resolution increased, we segment the optical image and use the pixel- based classification results to merge the object-oriented segmentation data, and finally get the rice extent map. At last, by using the time series analysis, the peak count was obtained for each rice area to ensure the crop intensity. In this work, the rice ground point from a GVG crowdsourcing smartphone and rice area statistical results from National Bureau of Statistics were used to validate and evaluate our result.

Global Coastal and Marine Spatial Planning (CMSP) from Space Based AIS Ship Tracking

NASA Astrophysics Data System (ADS)

Schwehr, K. D.; Foulkes, J. A.; Lorenzini, D.; Kanawati, M.

2011-12-01

All nations need to be developing long term integrated strategies for how to use and preserve our natural resources. As a part of these strategies, we must evalutate how communities of users react to changes in rules and regulations of ocean use. Global characterization of the vessel traffic on our Earth's oceans is essential to understanding the existing uses to develop international Coast and Marine Spatial Planning (CMSP). Ship traffic within 100-200km is beginning to be effectively covered in low latitudes by ground based receivers collecting position reports from the maritime Automatic Identification System (AIS). Unfortunately, remote islands, high latitudes, and open ocean Marine Protected Areas (MPA) are not covered by these ground systems. Deploying enough autonomous airborne (UAV) and surface (USV) vessels and buoys to provide adequate coverage is a difficult task. While the individual device costs are plummeting, a large fleet of AIS receivers is expensive to maintain. The global AIS coverage from SpaceQuest's low Earth orbit satellite receivers combined with the visualization and data storage infrastructure of Google (e.g. Maps, Earth, and Fusion Tables) provide a platform that enables researchers and resource managers to begin answer the question of how ocean resources are being utilized. Near real-time vessel traffic data will allow managers of marine resources to understand how changes to education, enforcement, rules, and regulations alter usage and compliance patterns. We will demonstrate the potential for this system using a sample SpaceQuest data set processed with libais which stores the results in a Fusion Table. From there, the data is imported to PyKML and visualized in Google Earth with a custom gx:Track visualization utilizing KML's extended data functionality to facilitate ship track interrogation. Analysts can then annotate and discuss vessel tracks in Fusion Tables.

Exploring Research Contributions of the North American Carbon Program using Google Earth and Google Map

NASA Astrophysics Data System (ADS)

Griffith, P. C.; Wilcox, L. E.; Morrell, A.

2009-12-01

The central objective of the North American Carbon Program (NACP), a core element of the US Global Change Research Program, is to quantify the sources and sinks of carbon dioxide, carbon monoxide, and methane in North America and adjacent ocean regions. The NACP consists of a wide range of investigators at universities and federal research centers. Although many of these investigators have worked together in the past, many have had few prior interactions and may not know of similar work within knowledge domains, much less across the diversity of environments and scientific approaches in the Program. Coordinating interactions and sharing data are major challenges in conducting NACP. The Google Earth and Google Map Collections on the NACP website (www.nacarbon.org) provide a geographical view of the research products contributed by each core and affiliated NACP project. Other relevant data sources (e.g. AERONET, LVIS) can also be browsed in spatial context with NACP contributions. Each contribution links to project-oriented metadata, or “project profiles”, that provide a greater understanding of the scientific and social context of each dataset and are an important means of communicating within the NACP and to the larger carbon cycle science community. Project profiles store information such as a project's title, leaders, participants, an abstract, keywords, funding agencies, associated intensive campaigns, expected data products, data needs, publications, and URLs to associated data centers, datasets, and metadata. Data products are research contributions that include biometric inventories, flux tower estimates, remote sensing land cover products, tools, services, and model inputs / outputs. Project leaders have been asked to identify these contributions to the site level whenever possible, either through simple latitude/longitude pair, or by uploading a KML, KMZ, or shape file. Project leaders may select custom icons to graphically categorize their contributions; for example, a ship for oceanographic samples, a tower for tower measurements. After post-processing, research contributions are added to the NACP Google Earth and Google Map Collection to facilitate discovery and use in synthesis activities of the Program.

Quantifying Urban Texture in Nairobi, Kenya and its Implications for Understanding Natural Hazard Impact

NASA Astrophysics Data System (ADS)

Taylor, Faith E.; Malamud, Bruce D.; Millington, James D. A.

2016-04-01

The configuration of infrastructure networks such as roads, drainage and power lines can both affect and be affected by natural hazards such as earthquakes, intense rain, wildfires and extreme temperatures. In this paper, we present and compare two methods to quantify urban topology on approximate scales of 0.0005 km2 to 10 km2 and create classifications of different 'urban textures' that relate to risk of natural hazard impact in an area. The methods we use focus on applicability in urban developing country settings, where access to high resolution and high quality data may be difficult. We use the city of Nairobi, Kenya to trial these methods. Nairobi has a population >3 million, and is a mix of informal settlements, residential and commercial development. The city and its immediate surroundings are subject to a variety of natural hazards such as floods, landslides, fires, drought, hail, heavy wind and extreme temperatures; all of these hazards can occur singly, but also have the potential for one to trigger another, thus providing a 'cascade' of hazards, or for two of the hazards to occur spatially and temporally near each other and interact. We use two measures of urban texture: (i) Street block textures, (ii) Google Earth land cover textures. Street block textures builds on the methodology of Louf and Barthelemy (2014) and uses Open Street Map data to analyse the shape, size, complexity and pattern of individual blocks of land created by fully enclosed loops of the major and minor road network of Nairobi. We find >4000 of these blocks ranging in size from approximately 0.0005 km2 to 10 km2, with approximately 5 classifications of urban texture. Google Earth land cover texture is a visual classification of homogeneous parcels of land performed in Google Earth using high-resolution airborne imagery and a qualitative criteria for each land cover type. Using the Google Earth land cover texture method, we identify >40 'urban textures' based on visual characteristics such as colour, texture, shadow and setting and have created a clear criteria for classifying an area based on its visual characteristics. These two methods for classifying urban texture in Nairobi are compared in a GIS and in the field to investigate whether there is a link between the visual appearance of an area and its network topology. From these urban textures, we may start to identify areas where (a) urban texture types may indicate a relative propensity to certain hazards and their interactions and (b) urban texture types that may increase or decrease the impact of a hazard that occurs in that area.

A proposed-standard format to represent and distribute tomographic models and other earth spatial data

NASA Astrophysics Data System (ADS)

Postpischl, L.; Morelli, A.; Danecek, P.

2009-04-01

Formats used to represent (and distribute) tomographic earth models differ considerably and are rarely self-consistent. In fact, each earth scientist, or research group, uses specific conventions to encode the various parameterizations used to describe, e.g., seismic wave speed or density in three dimensions, and complete information is often found in related documents or publications (if available at all) only. As a consequence, use of various tomographic models from different authors requires considerable effort, is more cumbersome than it should be and prevents widespread exchange and circulation within the community. We propose a format, based on modern web standards, able to represent different (grid-based) model parameterizations within the same simple text-based environment, easy to write, to parse, and to visualise. The aim is the creation of self-describing data-structures, both human and machine readable, that are automatically recognised by general-purpose software agents, and easily imported in the scientific programming environment. We think that the adoption of such a representation as a standard for the exchange and distribution of earth models can greatly ease their usage and enhance their circulation, both among fellow seismologists and among a broader non-specialist community. The proposed solution uses semantic web technologies, fully fitting the current trends in data accessibility. It is based on Json (JavaScript Object Notation), a plain-text, human-readable lightweight computer data interchange format, which adopts a hierarchical name-value model for representing simple data structures and associative arrays (called objects). Our implementation allows integration of large datasets with metadata (authors, affiliations, bibliographic references, units of measure etc.) into a single resource. It is equally suited to represent other geo-referenced volumetric quantities — beyond tomographic models — as well as (structured and unstructured) computational meshes. This approach can exploit the capabilities of the web browser as a computing platform: a series of in-page quick tools for comparative analysis between models will be presented, as well as visualisation techniques for tomographic layers in Google Maps and Google Earth. We are working on tools for conversion into common scientific format like netCDF, to allow easy visualisation in GEON-IDV or gmt.

Machine Learning for Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Kuhn, C.; Tellman, B.; Max, S. A.; Schwarz, B.

2015-12-01

With the increasing availability of high-resolution satellite imagery, dynamic flood mapping in near real time is becoming a reachable goal for decision-makers. This talk describes a newly developed framework for predicting biophysical flood vulnerability using public data, cloud computing and machine learning. Our objective is to define an approach to flood inundation modeling using statistical learning methods deployed in a cloud-based computing platform. Traditionally, static flood extent maps grounded in physically based hydrologic models can require hours of human expertise to construct at significant financial cost. In addition, desktop modeling software and limited local server storage can impose restraints on the size and resolution of input datasets. Data-driven, cloud-based processing holds promise for predictive watershed modeling at a wide range of spatio-temporal scales. However, these benefits come with constraints. In particular, parallel computing limits a modeler's ability to simulate the flow of water across a landscape, rendering traditional routing algorithms unusable in this platform. Our project pushes these limits by testing the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forests, at predicting flood extent. Constructed in Google Earth Engine, the model mines a suite of publicly available satellite imagery layers to use as algorithm inputs. Results are cross-validated using MODIS-based flood maps created using the Dartmouth Flood Observatory detection algorithm. Model uncertainty highlights the difficulty of deploying unbalanced training data sets based on rare extreme events.

Towards the Ubiquitous Deployment of DNSSEC

DTIC Science & Technology

2016-01-01

with other deployment partners around the world, there is now a significant and growing number of TLDs that have been signed, and a number of...as Google Earth, the Blackberry 10 operating system, and the entire set of K Desktop Environment (KDE) windowing system applications are based on...differentiate between transient errors and legitimate DNS spoofing attacks is likely going to be very important as deployment grows . The importance of

Using NASA's Giovanni Web Portal to Access and Visualize Satellite-based Earth Science Data in the Classroom

NASA Technical Reports Server (NTRS)

Lloyd, Steven; Acker, James G.; Prados, Ana I.; Leptoukh, Gregory G.

2008-01-01

One of the biggest obstacles for the average Earth science student today is locating and obtaining satellite-based remote sensing data sets in a format that is accessible and optimal for their data analysis needs. At the Goddard Earth Sciences Data and Information Services Center (GES-DISC) alone, on the order of hundreds of Terabytes of data are available for distribution to scientists, students and the general public. The single biggest and time-consuming hurdle for most students when they begin their study of the various datasets is how to slog through this mountain of data to arrive at a properly sub-setted and manageable data set to answer their science question(s). The GES DISC provides a number of tools for data access and visualization, including the Google-like Mirador search engine and the powerful GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) web interface.

Playing with Satellite Data

NASA Astrophysics Data System (ADS)

Beitler, J.; Truex, S.

2008-12-01

Would you like to see your science on the evening news? On everyone's mobile device? How hard is it to make one of those cool Google Earth files so people can explore your world? Do you need to be a programmer, or could most any person with a little motivation and a few inexpensive tools do it? Find out what it takes to get started with these technologies--it may be easier than you think--and how they can give your data more legs. I will demonstrate some of the ways that the National Snow and Ice Data Center has been successful in reaching the public and educators with visualized and animated data about the Earth's frozen regions, and talk about some of the how-to. In particular, see what we have done with QuickTime, Google Earth, YouTube, and the iPhone. I'll also talk about how we've assessed the reach and success of these efforts.

Spatiotemporal Visualization of Tsunami Waves Using Kml on Google Earth

NASA Astrophysics Data System (ADS)

Mohammadi, H.; Delavar, M. R.; Sharifi, M. A.; Pirooz, M. D.

2017-09-01

Disaster risk is a function of hazard and vulnerability. Risk is defined as the expected losses, including lives, personal injuries, property damages, and economic disruptions, due to a particular hazard for a given area and time period. Risk assessment is one of the key elements of a natural disaster management strategy as it allows for better disaster mitigation and preparation. It provides input for informed decision making, and increases risk awareness among decision makers and other stakeholders. Virtual globes such as Google Earth can be used as a visualization tool. Proper spatiotemporal graphical representations of the concerned risk significantly reduces the amount of effort to visualize the impact of the risk and improves the efficiency of the decision-making process to mitigate the impact of the risk. The spatiotemporal visualization of tsunami waves for disaster management process is an attractive topic in geosciences to assist investigation of areas at tsunami risk. In this paper, a method for coupling virtual globes with tsunami wave arrival time models is presented. In this process we have shown 2D+Time of tsunami waves for propagation and inundation of tsunami waves, both coastal line deformation, and the flooded areas. In addition, the worst case scenario of tsunami on Chabahar port derived from tsunami modelling is also presented using KML on google earth.

Integrating and Visualizing Tropical Cyclone Data Using the Real Time Mission Monitor

NASA Technical Reports Server (NTRS)

Goodman, H. Michael; Blakeslee, Richard; Conover, Helen; Hall, John; He, Yubin; Regner, Kathryn

2009-01-01

The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the NASA Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual globe application. RTMM is extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, scientists, and managers appreciate the contributions that RTMM makes to their flight projects. A broad spectrum of interdisciplinary scientists used RTMM during field campaigns including the hurricane-focused 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 NOAA-NASA Aerosonde Hurricane Noel flight, 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), plus a soil moisture (SMAP-VEX) and two arctic research experiments (ARCTAS) in 2008. Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated "on the fly". The resultant flight plan is then immediately posted to the Google Earth-based RTMM for interested scientists to view the planned flight track and subsequently compare it to the actual real time flight progress. We are planning additional capabilities to RTMM including collaborations with the Jet Propulsion Laboratory in the joint development of a Tropical Cyclone Integrated Data Exchange and Analysis System (TC IDEAS) which will serve as a web portal for access to tropical cyclone data, visualizations and model output.

Virtual Field Trips: Using Google Maps to Support Online Learning and Teaching of the History of Astronomy

ERIC Educational Resources Information Center

Fluke, Christopher J.

2009-01-01

I report on a pilot study on the use of Google Maps to provide virtual field trips as a component of a wholly online graduate course on the history of astronomy. The Astronomical Tourist Web site (http://astronomy.swin.edu.au/sao/tourist), themed around the role that specific locations on Earth have contributed to the development of astronomical…

Leveraging Global Geo-Data and Information Technologies to Bring Authentic Research Experiences to Students in Introductory Geosciences Courses

NASA Astrophysics Data System (ADS)

Ryan, J. G.

2014-12-01

The 2012 PCAST report identified the improvement of "gateway" science courses as critical to increasing the number of STEM graduates to levels commensurate with national needs. The urgent need to recruit/ retain more STEM graduates is particularly acute in the geosciences, where growth in employment opportunities, an aging workforce and flat graduation rates are leading to substantial unmet demand for geoscience-trained STEM graduates. The need to increase the number of Bachelors-level geoscience graduates was an identified priority at the Summit on the Future of Undergraduate Geoscience Education (http://www.jsg.utexas.edu/events/future-of-geoscience-undergraduateeducation/), as was the necessity of focusing on 2-year colleges, where a growing number of students are being introduced to geosciences. Undergraduate research as an instructional tool can help engage and retain students, but has largely not been part of introductory geoscience courses because of the challenge of scaling such activities for large student numbers. However, burgeoning information technology resources, including publicly available earth and planetary data repositories and freely available, intuitive data visualization platforms makes structured, in-classroom investigations of geoscience questions tractable, and open-ended student inquiry possible. Examples include "MARGINS Mini-Lessons", instructional resources developed with the support of two NSF-DUE grant awards that involve investigations of marine geosciences data resources (overseen by the Integrated Earth Data Applications (IEDA) portal: www.iedadata.org) and data visualization using GeoMapApp (www.geomapapp.org); and the growing suite of Google-Earth based data visualization and exploration activities overseen by the Google Earth in Onsite and Distance Education project (geode.net). Sample-based investigations are also viable in introductory courses, thanks to remote instrument operations technologies that allow real student participation in instrument-based data collection and interpretation. It is thus possible to model for students nearly the entire scientific process in introductory geoscience courses, allowing them to experience the excitement of "doing" science and thereby enticing more of them into the field.

Free Global Dsm Assessment on Large Scale Areas Exploiting the Potentialities of the Innovative Google Earth Engine Platform

NASA Astrophysics Data System (ADS)

Nascetti, A.; Di Rita, M.; Ravanelli, R.; Amicuzi, M.; Esposito, S.; Crespi, M.

2017-05-01

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

NARSTO EPA SS PITTSBURGH GAS PM PROPERTY DATA

Atmospheric Science Data Center

2018-04-09

... Sizer Nephelometer Aerosol Collector SMPS - Scanning Mobility Particle Sizer Fluorescence Spectroscopy ... Get Google Earth Related Data:  Environmental Protection Agency Supersites Pittsburgh, Pennsylvania ...

[Establishment of malaria early warning system in Jiangsu Province II application of digital earth system in malaria epidemic management and surveillance].

PubMed

Wang, Wei-Ming; Zhou, Hua-Yun; Liu, Yao-Bao; Li, Ju-Lin; Cao, Yuan-Yuan; Cao, Jun

2013-04-01

To explore a new mode of malaria elimination through the application of digital earth system in malaria epidemic management and surveillance. While we investigated the malaria cases and deal with the epidemic areas in Jiangsu Province in 2011, we used JISIBAO UniStrong G330 GIS data acquisition unit (GPS) to collect the latitude and longitude of the cases located, and then established a landmark library about early-warning areas and an image management system by using Google Earth Free 6.2 and its image processing software. A total of 374 malaria cases were reported in Jiangsu Province in 2011. Among them, there were 13 local vivax malaria cases, 11 imported vivax malaria cases from other provinces, 20 abroad imported vivax malaria cases, 309 abroad imported falciparum malaria cases, 7 abroad imported quartan malaria cases (Plasmodium malaria infection), and 14 abroad imported ovale malaria cases (P. ovale infection). Through the analysis of Google Earth Mapping system, these malaria cases showed a certain degree of aggregation except the abroad imported quartan malaria cases which were highly sporadic. The local vivax malaria cases mainly concentrated in Sihong County, the imported vivax malaria cases from other provinces mainly concentrated in Suzhou City and Wuxi City, the abroad imported vivax malaria cases concentrated in Nanjing City, the abroad imported falciparum malaria cases clustered in the middle parts of Jiangsu Province, and the abroad imported ovale malaria cases clustered in Liyang City. The operation of Google Earth Free 6.2 is simple, convenient and quick, which could help the public health authority to make the decision of malaria prevention and control, including the use of funds and other health resources.

Assessing Coupled Social Ecological Flood Vulnerability from Uttarakhand, India, to the State of New York with Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Schwarz, B.

2014-12-01

This talk describes the development of a web application to predict and communicate vulnerability to floods given publicly available data, disaster science, and geotech cloud capabilities. The proof of concept in Google Earth Engine API with initial testing on case studies in New York and Utterakhand India demonstrates the potential of highly parallelized cloud computing to model socio-ecological disaster vulnerability at high spatial and temporal resolution and in near real time. Cloud computing facilitates statistical modeling with variables derived from large public social and ecological data sets, including census data, nighttime lights (NTL), and World Pop to derive social parameters together with elevation, satellite imagery, rainfall, and observed flood data from Dartmouth Flood Observatory to derive biophysical parameters. While more traditional, physically based hydrological models that rely on flow algorithms and numerical methods are currently unavailable in parallelized computing platforms like Google Earth Engine, there is high potential to explore "data driven" modeling that trades physics for statistics in a parallelized environment. A data driven approach to flood modeling with geographically weighted logistic regression has been initially tested on Hurricane Irene in southeastern New York. Comparison of model results with observed flood data reveals a 97% accuracy of the model to predict flooded pixels. Testing on multiple storms is required to further validate this initial promising approach. A statistical social-ecological flood model that could produce rapid vulnerability assessments to predict who might require immediate evacuation and where could serve as an early warning. This type of early warning system would be especially relevant in data poor places lacking the computing power, high resolution data such as LiDar and stream gauges, or hydrologic expertise to run physically based models in real time. As the data-driven model presented relies on globally available data, the only real time data input required would be typical data from a weather service, e.g. precipitation or coarse resolution flood prediction. However, model uncertainty will vary locally depending upon the resolution and frequency of observed flood and socio-economic damage impact data.

The Real Time Mission Monitor: A Platform for Real Time Environmental Data Integration and Display during NASA Field Campaigns

NASA Astrophysics Data System (ADS)

He, M.; Hardin, D. M.; Goodman, M.; Blakeslee, R.

2008-05-01

The Real Time Mission Monitor (RTMM) is an interactive visualization application based on Google Earth, that provides situational awareness and field asset management during NASA field campaigns. The RTMM can integrate data and imagery from numerous sources including GOES-12, GOES-10, and TRMM satellites. Simultaneously, it can display data and imagery from surface observations including Nexrad, NPOL and SMART- R radars. In addition to all these it can display output from models and real-time flight tracks of all aircraft involved in the experiment. In some instances the RTMM can also display measurements from scientific instruments as they are being flown. All data are recorded and archived in an on-line system enabling playback and review of all sorties. This is invaluable in preparing for future deployments and in exercising case studies. The RTMM facilitates pre-flight planning, in-flight monitoring, development of adaptive flight strategies and post- flight data analyses and assessments. Since the RTMM is available via the internet - during the actual experiment - project managers, scientists and mission planners can collaborate no matter where they are located as long as they have a viable internet connection. In addition, the system is open so that the general public can also view the experiment, in-progress, with Google Earth. Predecessors of RTMM were originally deployed in 2002 as part of the Altus Cumulus Electrification Study (ACES) to monitor uninhabited aerial vehicles near thunderstorms. In 2005 an interactive Java-based web prototype supported the airborne Lightning Instrument Package (LIP) during the Tropical Cloud Systems and Processes (TCSP) experiment. In 2006 the technology was adapted to the 3D Google Earth virtual globe and in 2007 its capabilities were extended to support multiple NASA aircraft (ER-2, WB-57, DC-8) during Tropical Composition, Clouds and Climate Coupling (TC4) experiment and 2007 Summer Aerosonde field study. In April 2008 the RTMM will be flown in the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment to study the atmospheric composition in the Arctic.

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

NASA Astrophysics Data System (ADS)

Ghaffarian, S.; Ghaffarian, S.

2014-08-01

This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

A virtual, interactive and dynamic excursion in Google Earth on soil management and conservation (AgroGeovid)

NASA Astrophysics Data System (ADS)

Vanwalleghem, Tom; Giráldez, Juan Vicente

2013-04-01

Many courses on natural resources require hands-on practical knowledge and experience that students traditionally could only acquire by expensive and time-consuming field excursions. New technologies and social media however provide an interesting alternative to train students and help them improve their practical knowledge. AgroGeovid is a virtual excursion, based on Google Earth, Youtube, Facebook and Twitter that is aimed at agricultural engineering students, but equally useful for any student interested in soil management and conservation, e.g. geography, geology and environmental resources. Agrogeovid provides the framework for teachers and students to upload geotagged photos, comments and discussions. After the initial startup phase, where the teacher uploaded material on e.g. soil erosion phenomena, soil conservation structures and different soil management strategies under different agronomic systems, students contributed with their own material gathered throughout the academic year. All students decided to contribute via Facebook, in stead of Twitter, which was not known to most of them. The final result was a visual and dynamic tool which students could use to train and perfect skills adopted in the classroom using case-studies and examples from their immediate environment.

Using Google Earth to Study the Basic Characteristics of Volcanoes

ERIC Educational Resources Information Center

Schipper, Stacia; Mattox, Stephen

2010-01-01

Landforms, natural hazards, and the change in the Earth over time are common material in state and national standards. Volcanoes exemplify these standards and readily capture the interest and imagination of students. With a minimum of training, students can recognize erupted materials and types of volcanoes; in turn, students can relate these…

Active Fire Mapping Program

MedlinePlus

Active Fire Mapping Program Current Large Incidents (Home) New Large Incidents Fire Detection Maps MODIS Satellite Imagery VIIRS Satellite Imagery Fire Detection GIS Data Fire Data in Google Earth ...

NARSTO EPA SS LOS ANGELES SMPS DATA

Atmospheric Science Data Center

2018-04-09

... Ground Station Instrument:  SMPS - Scanning Mobility Particle Sizer Location:  Los Angeles, ... Get Google Earth Related Data:  Environmental Protection Agency Supersites Los Angeles, California ...

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

PubMed

Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Enhancements and Evolution of the Real Time Mission Monitor

NASA Astrophysics Data System (ADS)

Goodman, M.; Blakeslee, R.; Hardin, D.; Hall, J.; He, Y.; Regner, K.

2008-12-01

The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual earth application. RTMM has proven extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, mission scientists, instrument scientists and program managers alike appreciate the contributions that RTMM makes to their flight projects. RTMM has received numerous plaudits from a wide variety of scientists who used RTMM during recent field campaigns including the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) missions, the 2007-2008 NOAA-NASA Aerosonde Hurricane flights and the 2008 Soil Moisture Active-Passive Validation Experiment (SMAP-VEX). Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated for altitude, latitude, longitude, flight leg distance, cumulative distance, flight leg time, cumulative time, and satellite overpass intersections. The resultant flight plan is then generated in KML and quickly posted to the Google Earth-based RTMM for planning discussions, as well as comparisons to real time flight tracks in progress. A description of the system architecture, components, and applications along with reviews and animations of RTMM during the field campaigns, plus planned enhancements and future opportunities will be presented.

Authoring Tours of Geospatial Data With KML and Google Earth

NASA Astrophysics Data System (ADS)

Barcay, D. P.; Weiss-Malik, M.

2008-12-01

As virtual globes become widely adopted by the general public, the use of geospatial data has expanded greatly. With the popularization of Google Earth and other platforms, GIS systems have become virtual reality platforms. Using these platforms, a casual user can easily explore the world, browse massive data-sets, create powerful 3D visualizations, and share those visualizations with millions of people using the KML language. This technology has raised the bar for professionals and academics alike. It is now expected that studies and projects will be accompanied by compelling, high-quality visualizations. In this new landscape, a presentation of geospatial data can be the most effective form of advertisement for a project: engaging both the general public and the scientific community in a unified interactive experience. On the other hand, merely dumping a dataset into a virtual globe can be a disorienting, alienating experience for many users. To create an effective, far-reaching presentation, an author must take care to make their data approachable to a wide variety of users with varying knowledge of the subject matter, expertise in virtual globes, and attention spans. To that end, we present techniques for creating self-guided interactive tours of data represented in KML and visualized in Google Earth. Using these methods, we provide the ability to move the camera through the world while dynamically varying the content, style, and visibility of the displayed data. Such tours can automatically guide users through massive, complex datasets: engaging a broad user-base, and conveying subtle concepts that aren't immediately apparent when viewing the raw data. To the casual user these techniques result in an extremely compelling experience similar to watching video. Unlike video though, these techniques maintain the rich interactive environment provided by the virtual globe, allowing users to explore the data in detail and to add other data sources to the presentation.

Google Earth Grand Tour Themes

NASA Astrophysics Data System (ADS)

De Paor, D. G.; Whitmeyer, S. J.; Bentley, C.; Dordevic, M. M.

2014-12-01

As part of an NSF TUES Type 3 project entitled "Google Earth for Onsite and Distance Education (GEODE)," we are assembling a "Grand Tour" of locations on Earth and other terrestrial bodies that every geoscience student should know about and visit at least in virtual reality. Based on feedback from colleagues at previous meetings, we have identified nine Grand Tour themes: "Plates and Plumes," "Rocks and Regions," "Geology Through Time," "The Mapping Challenge*," "U.S. National Parks*," "The Magical Mystery Tour*," "Resources and Hazards," "Planets and Moons," and "Top of the Pops." Themes marked with an asterisk are most developed at this stage and will be demonstrated in real time. The Mapping Challenge invites students to trace geological contacts, measure bedding strike and dip and the plunge, trend, and facing of a fold. There is an advanced tool for modeling periclinal folds. The challenge is presented in a game-like format with an emphasis on puzzle-solving that will appeal to students regardless of gender. For the tour of U.S. national parks, we divided the most geologically important parks into four groups—Western Pacific, West Coast, Rockies, and East Coast. We are combining our own team's GigaPan imagery with imagery already available on the Internet. There is a great deal of imagery just waiting to be annotated for geological education purposes. The Magical Mystery Tour takes students to Google Streetview locations selected by instructors. Students are presented with questions or tasks and are given automatic feedback. Other themes are under development. Within each theme, we are crowd-sourcing contributions from colleagues and inviting colleagues to vote for or against proposed locations and student interactions. The GEODE team includes the authors and: Heather Almquist, Stephen Burgin, Cinzia Cervato, Gene Cooper, Paul Karabinos, Terry Pavlis, Jen Piatek, Bill Richards, Jeff Ryan, Ron Schott, Kristen St. John, and Barb Tewksbury.

Dagik: A Quick Look System of the Geospace Data in KML format

NASA Astrophysics Data System (ADS)

Yoshida, D.; Saito, A.

2007-12-01

Dagik (Daily Geospace data in KML) is a quick look plot sharing system using Google Earth as a data browser. It provides daily data lists that contain network links to the KML/KMZ files of various geospace data. KML is a markup language to display data on Google Earth, and KMZ is a compressed file of KML. Users can browse the KML/KMZ files with the following procedures: 1) download "dagik.kml" from Dagik homepage (http://www- step.kugi.kyoto-u.ac.jp/dagik/), and open it with Google Earth, 2) select date, 3) select data type to browse. Dagik is a collection of network links to KML/KMZ files. The daily Dagik files are available since 1957, though they contain only the geomagnetic index data in the early periods. There are three activities of Dagik. The first one is the generation of the daily data lists, the second is to provide several useful tools, such as observatory lists, and the third is to assist researchers to make KML/KMZ data plots. To make the plot browsing easy, there are three rules for Dagik plot format: 1) one file contains one UT day data, 2) use common plot panel size, 3) share the data list. There are three steps to join Dagik as a plot provider: 1) make KML/KMZ files of the data, 2) put the KML/KMZ files on Web, 3) notice Dagik group the URL address and description of the files. The KML/KMZ files will be included in Dagik data list. As of September 2007, quick looks of several geosphace data, such as GPS total electron content data, ionosonde data, magnetometer data, FUV imaging data by a satellite, ground-based airglow data, and satellite footprint data, are available. The system of Dagik is introduced in the presentation. u.ac.jp/dagik/

Brave New Media World: Science Communication Voyages through the Global Seas

NASA Astrophysics Data System (ADS)

Clark, C. L.; Reisewitz, A.

2010-12-01

By leveraging online tools, such as blogs, Twitter, Facebook, Google Earth, flickr, web-based discussion boards, and a bi-monthly electronic magazine for the non-scientist, Scripps Institution of Oceanography is taking science communications out of the static webpage to create interactive journeys that spark social dialogue and helped raise awareness of science-based research on global marine environmental issues. Several new initiatives are being chronicled through popular blogs and expedition web sites as researchers share interesting scientific facts and unusual findings in near real-time.

Relevant Links

Atmospheric Science Data Center

2018-06-15

... Theoretical Basis Document (ATBD) ADAM-M ADAM-M Information AirMISR AirMISR Home Page MISR Home Page Feature Article: Fiery Temperament KONVEX Information SAFARI Home Page AirMSPI Get Google Earth ...

A Web-Based Interactive Mapping System of State Wide School Performance: Integrating Google Maps API Technology into Educational Achievement Data

ERIC Educational Resources Information Center

Wang, Kening; Mulvenon, Sean W.; Stegman, Charles; Anderson, Travis

2008-01-01

Google Maps API (Application Programming Interface), released in late June 2005 by Google, is an amazing technology that allows users to embed Google Maps in their own Web pages with JavaScript. Google Maps API has accelerated the development of new Google Maps based applications. This article reports a Web-based interactive mapping system…

Visualizing spatio-temporal war casualty data in Google Earth - A case study of Map the Fallen (Invited)

NASA Astrophysics Data System (ADS)

Askay, S.

2009-12-01

Published on Memorial Day 2009, Map the Fallen is a Google Earth visualization of the 5500+ US and international soldiers that have died in Iraq and Afghanistan since 2001. In addition to providing photos, stories and links for each solider, the time-animated map visually connects hometowns to places of death. This novel way of representing casualty data brings the geographic reach and magnitude of the issue into focus together with the very personal nature of individual stories. Innovative visualizations techniques were used that illustrate the spatio-temporal nature of this information and to show the global reach and interconnectivity of this issue. Several of advanced KML techniques employed to create this engaging and performance-conscious map will be discussed during this session. These include: 1) the use of HTML iframes and javascript to minimize the KML size, and extensive cross-linking throughout content; 2) the creation of a time-animated, on-screen casualty counter; 3) the use of parabolic arcs to connect each hometown to place of death; 4) the use of concentric spirals to represent chronological data; and 5) numerous performance optimizations to ensure the 23K placemarks, 2500 screen overlays and nearly 250k line vertices performed well in Google Earth. This session will include a demonstration of the map, conceptual discussions of the techniques used, and some in-depth technical explanation of the KML code.

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

NASA Astrophysics Data System (ADS)

Yang, Xue; Sun, Hao; Fu, Kun; Yang, Jirui; Sun, Xian; Yan, Menglong; Guo, Zhi

2018-01-01

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

The impact of land use changes in the Banjarsari village, Cerme district of Gresik Regency, East Java Province

NASA Astrophysics Data System (ADS)

Ayu Larasati, Dian; Hariyanto, Bambang

2018-01-01

High population growth, and development activities in various fields will lead to join the growing demand for land. Cerme is a district close to the city of Surabaya, therefore a lot of agricultural land in Cerme used as housing and industry in order to support the growth of the population whose land in Surabaya city could not accommodate more. Base on this fact the research be did. The aim of this research is: determine the pattern of land use changes in the last year and to analyze the socioeconomic changes in the Banjarsari village, Gresik Regency. To determine the socioeconomic changes in the area of research is required: a). population change data from 2010 to 2015, b). Google Earth Imagery 2010 to 2015. The population data and the type of work changes are described by the time series and land cover change analysis. To analysis the land use conversion we also use Google Earth imagery with ArcGIS applications. For astronomical layout correction based on GPS field checks and RBI Map. The goal of this study is 1). Farmland change into residential/settlements in 2004-2014 is 12%; 2). Peoples who changing their livelihood is 39%. In occupational changes affect the population income ranges from 500,000 IDR -. 1,000,000 IDR per month/percapita.

Streets? Where We're Going, We Don't Need Streets

NASA Astrophysics Data System (ADS)

Bailey, J.

2017-12-01

In 2007 Google Street View started as a project to provide 360-degree imagery along streets, but in the decade since has evolved into a platform through which to explore everywhere from the slope of everest, to the middle of the Amazon rainforest to under the ocean. As camera technology has evolved it has also become a tool for ground truthing maps, and provided scientific observations, storytelling and education. The Google Street View "special collects" team has undertaken increasingly more challenging projects across 80+ countries and every continent. All of which culminated in possibly the most ambitious collection yet, the capture of Street View on board the International Space Station. Learn about the preparation and obstacles behind this and other special collects. Explore these datasets through both Google Earth and Google Expeditions VR, an educational tool to take students on virtual field trips using 360 degree imagery.

Accelerating North American rangeland conservation with earth observation data and user driven web applications.

NASA Astrophysics Data System (ADS)

Allred, B. W.; Naugle, D.; Donnelly, P.; Tack, J.; Jones, M. O.

2016-12-01

In 2010, the USDA Natural Resources Conservation Service (NRCS) launched the Sage Grouse Initiative (SGI) to voluntarily reduce threats facing sage-grouse and rangelands on private lands. Over the past five years, SGI has matured into a primary catalyst for rangeland and wildlife conservation across the North American west, focusing on the shared vision of wildlife conservation through sustainable working landscapes and providing win-win solutions for producers, sage grouse, and 350 other sagebrush obligate species. SGI and its partners have invested a total of $750 million into rangeland and wildlife conservation. Moving forward, SGI continues to focus on rangeland conservation. Partnering with Google Earth Engine, SGI has developed outcome monitoring and conservation planning tools at continental scales. The SGI science team is currently developing assessment and monitoring algorithms of key conservation indicators. The SGI web application utilizes Google Earth Engine for user defined analysis and planning, putting the appropriate information directly into the hands of managers and conservationists.

The ethics of Google Earth: crossing thresholds from spatial data to landscape visualisation.

PubMed

Sheppard, Stephen R J; Cizek, Petr

2009-05-01

'Virtual globe' software systems such as Google Earth are growing rapidly in popularity as a way to visualise and share 3D environmental data. Scientists and environmental professionals, many of whom are new to 3D modeling and visual communications, are beginning routinely to use such techniques in their work. While the appeal of these techniques is evident, with unprecedented opportunities for public access to data and collaborative engagement over the web, are there nonetheless risks in their widespread usage when applied in areas of the public interest such as planning and policy-making? This paper argues that the Google Earth phenomenon, which features realistic imagery of places, cannot be dealt with only as a question of spatial data and geographic information science. The virtual globe type of visualisation crosses several key thresholds in communicating scientific and environmental information, taking it well beyond the realm of conventional spatial data and geographic information science, and engaging more complex dimensions of human perception and aesthetic preference. The realism, perspective views, and social meanings of the landscape visualisations embedded in virtual globes invoke not only cognition but also emotional and intuitive responses, with associated issues of uncertainty, credibility, and bias in interpreting the imagery. This paper considers the types of risks as well as benefits that may exist with participatory uses of virtual globes by experts and lay-people. It is illustrated with early examples from practice and relevant themes from the literature in landscape visualisation and related disciplines such as environmental psychology and landscape planning. Existing frameworks and principles for the appropriate use of environmental visualisation methods are applied to the special case of widely accessible, realistic 3D and 4D visualisation systems such as Google Earth, in the context of public awareness-building and agency decision-making on environmental issues. Relevant principles are suggested which lend themselves to much-needed evaluation of risks and benefits of virtual globe systems. Possible approaches for balancing these benefits and risks include codes of ethics, software design, and metadata templates.

Learning GIS and exploring geolocated data with the all-in-one Geolokit toolbox for Google Earth

NASA Astrophysics Data System (ADS)

Watlet, A.; Triantafyllou, A.; Bastin, C.

2016-12-01

GIS software are today's essential tools to gather and visualize geological data, to apply spatial and temporal analysis and finally, to create and share interactive maps for further investigations in geosciences. Such skills are especially essential to learn for students who go through fieldtrips, samples collections or field experiments. However, time is generally missing to teach in detail all the aspects of visualizing geolocated geoscientific data. For these purposes, we developed Geolokit: a lightweight freeware dedicated to geodata visualization and written in Python, a high-level, cross-platform programming language. Geolokit software is accessible through a graphical user interface, designed to run in parallel with Google Earth, benefitting from the numerous interactive capabilities. It is designed as a very user-friendly toolbox that allows `geo-users' to import their raw data (e.g. GPS, sample locations, structural data, field pictures, maps), to use fast data analysis tools and to visualize these into the Google Earth environment using KML code; with no require of third party software, except Google Earth itself. Geolokit comes with a large number of geosciences labels, symbols, colours and placemarks and is applicable to display several types of geolocated data, including: Multi-points datasets Automatically computed contours of multi-points datasets via several interpolation methods Discrete planar and linear structural geology data in 2D or 3D supporting large range of structures input format Clustered stereonets and rose diagrams 2D cross-sections as vertical sections Georeferenced maps and grids with user defined coordinates Field pictures using either geo-tracking metadata from a camera built-in GPS module, or the same-day track of an external GPS In the end, Geolokit is helpful for quickly visualizing and exploring data without losing too much time in the numerous capabilities of GIS software suites. We are looking for students and teachers to discover all the functionalities of Geolokit. As this project is under development and planned to be open source, we are definitely looking to discussions regarding particular needs or ideas, and to contributions in the Geolokit project.

The Use of Virtual Globes as a Spatial Teaching Tool with Suggestions for Metadata Standards

ERIC Educational Resources Information Center

Schultz, Richard B.; Kerski, Joseph J.; Patterson, Todd C.

2008-01-01

Virtual Globe software has become extremely popular both inside and outside of educational settings. This software allows users to explore the Earth in three dimensions while streaming satellite imagery, elevation, and other data from the Internet. Virtual Globes, such as Google Earth, NASA World Wind, and ESRI's ArcGIS Explorer can be effectively…

Enhancements and Evolution of the Real Time Mission Monitor

NASA Technical Reports Server (NTRS)

Goodman, Michael; Blakeslee, Richard; Hardin, Danny; Hall, John; He, Yubin; Regner, Kathryn

2008-01-01

The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual globe application. RTMM has proven extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, mission scientists, instrument scientists and program managers alike appreciate the contributions that RTMM makes to their flight projects. We have received numerous plaudits from a wide variety of scientists who used RTMM during recent field campaigns including the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) missions, the 2007-2008 NOAA-NASA Aerosonde Hurricane flights and the 2008 Soil Moisture Active-Passive Validation Experiment (SMAP-VEX). Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated for altitude, latitude, longitude, flight leg distance, cumulative distance, flight leg time, cumulative time, and satellite overpass intersections. The resultant flight plan is then generated in KML and quickly posted to the Google Earth-based RTMM for interested scientists to view the planned flight track and then compare it to the actual real time flight progress. A description of the system architecture, components, and applications along with reviews and animations of RTMM during the field campaigns, plus planned enhancements and future opportunities will be presented.

Copernicus Big Data and Google Earth Engine for Glacier Surface Velocity Field Monitoring: Feasibility Demonstration on San Rafael and San Quintin Glaciers

NASA Astrophysics Data System (ADS)

Di Tullio, M.; Nocchi, F.; Camplani, A.; Emanuelli, N.; Nascetti, A.; Crespi, M.

2018-04-01

The glaciers are a natural global resource and one of the principal climate change indicator at global and local scale, being influenced by temperature and snow precipitation changes. Among the parameters used for glacier monitoring, the surface velocity is a key element, since it is connected to glaciers changes (mass balance, hydro balance, glaciers stability, landscape erosion). The leading idea of this work is to continuously retrieve glaciers surface velocity using free ESA Sentinel-1 SAR imagery and exploiting the potentialities of the Google Earth Engine (GEE) platform. GEE has been recently released by Google as a platform for petabyte-scale scientific analysis and visualization of geospatial datasets. The algorithm of SAR off-set tracking developed at the Geodesy and Geomatics Division of the University of Rome La Sapienza has been integrated in a cloud based platform that automatically processes large stacks of Sentinel-1 data to retrieve glacier surface velocity field time series. We processed about 600 Sentinel-1 image pairs to obtain a continuous time series of velocity field measurements over 3 years from January 2015 to January 2018 for two wide glaciers located in the Northern Patagonian Ice Field (NPIF), the San Rafael and the San Quintin glaciers. Several results related to these relevant glaciers also validated with respect already available and renown software (i.e. ESA SNAP, CIAS) and with respect optical sensor measurements (i.e. LANDSAT8), highlight the potential of the Big Data analysis to automatically monitor glacier surface velocity fields at global scale, exploiting the synergy between GEE and Sentinel-1 imagery.

Local Air Quality Conditions and Forecasts

MedlinePlus

... Monitor Location Archived Maps by Region Canada Air Quality Air Quality on Google Earth Links A-Z About AirNow AirNow International Air Quality Action Days / Alerts AirCompare Air Quality Index (AQI) ...

What's New in the Ocean in Google Earth and Maps

NASA Astrophysics Data System (ADS)

Austin, J.; Sandwell, D. T.

2014-12-01

Jenifer Austin, Jamie Adams, Kurt Schwehr, Brian Sullivan, David Sandwell2, Walter Smith3, Vicki Ferrini4, and Barry Eakins5, 1 Google Inc., 1600 Amphitheatre Parkway, Mountain View, California, USA 2 University of California-San Diego, Scripps Institute of Oceanography, La Jolla, California ,USA3 NOAA Laboratory for Satellite Altimetry, College Park, Maryland, USA4 Lamont Doherty, Columbia University5 NOAAMore than two-thirds of Earth is covered by oceans. On the almost 6 year anniversary of launching an explorable ocean seafloor in Google Earth and Maps, we updated our global underwater terrain dataset in partnership with Lamont-Doherty at Columbia, the Scripps Institution of Oceanography, and NOAA. With this update to our ocean map, we'll reveal an additional 2% of the ocean in high resolution representing 2 years of work by Columbia, pulling in data from numerous institutions including the Campeche Escarpment in the Gulf of Mexico in partnership with Charlie Paul at MBARI and the Schmidt Ocean Institute. The Scripps Institution of Oceanography at UCSD has curated 30 years of data from more than 8,000 ship cruises and 135 different institutions to reveal 15 percent of the seafloor at 1 km resolution. In addition, explore new data from an automated pipeline built to make updates to our Ocean Map more scalable in partnership with NOAA's National Geophysical Data Center (link to http://www.ngdc.noaa.gov/mgg/bathymetry/) and the University of Colorado CIRES program (link to http://cires.colorado.edu/index.html).

Epidemiologic study of residential proximity to transmission lines and childhood cancer in California: description of design, epidemiologic methods and study population

PubMed Central

Kheifets, Leeka; Crespi, Catherine M; Hooper, Chris; Oksuzyan, Sona; Cockburn, Myles; Ly, Thomas; Mezei, Gabor

2015-01-01

We conducted a large epidemiologic case-control study in California to examine the association between childhood cancer risk and distance from the home address at birth to the nearest high-voltage overhead transmission line as a replication of the study of Draper et al. in the United Kingdom. We present a detailed description of the study design, methods of case ascertainment, control selection, exposure assessment and data analysis plan. A total of 5788 childhood leukemia cases and 3308 childhood central nervous system cancer cases (included for comparison) and matched controls were available for analysis. Birth and diagnosis addresses of cases and birth addresses of controls were geocoded. Distance from the home to nearby overhead transmission lines was ascertained on the basis of the electric power companies’ geographic information system (GIS) databases, additional Google Earth aerial evaluation and site visits to selected residences. We evaluated distances to power lines up to 2000 m and included consideration of lower voltages (60–69 kV). Distance measures based on GIS and Google Earth evaluation showed close agreement (Pearson correlation >0.99). Our three-tiered approach to exposure assessment allowed us to achieve high specificity, which is crucial for studies of rare diseases with low exposure prevalence. PMID:24045429

Visualization and Ontology of Geospatial Intelligence

NASA Astrophysics Data System (ADS)

Chan, Yupo

Recent events have deepened our conviction that many human endeavors are best described in a geospatial context. This is evidenced in the prevalence of location-based services, as afforded by the ubiquitous cell phone usage. It is also manifested by the popularity of such internet engines as Google Earth. As we commute to work, travel on business or pleasure, we make decisions based on the geospatial information provided by such location-based services. When corporations devise their business plans, they also rely heavily on such geospatial data. By definition, local, state and federal governments provide services according to geographic boundaries. One estimate suggests that 85 percent of data contain spatial attributes.

Digital Earth reloaded - Beyond the next generation

NASA Astrophysics Data System (ADS)

Ehlers, M.; Woodgate, P.; Annoni, A.; Schade, S.

2014-02-01

Digital replicas (or 'mirror worlds') of complex entities and systems are now routine in many fields such as aerospace engineering; archaeology; medicine; or even fashion design. The Digital Earth (DE) concept as a digital replica of the entire planet occurs in Al Gore's 1992 book Earth in the Balance and was popularized in his speech at the California Science Center in January 1998. It played a pivotal role in stimulating the development of a first generation of virtual globes, typified by Google Earth that achieved many elements of this vision. Almost 15 years after Al Gore's speech, the concept of DE needs to be re-evaluated in the light of the many scientific and technical developments in the fields of information technology, data infrastructures, citizen?s participation, and earth observation that have taken place since. This paper intends to look beyond the next generation predominantly based on the developments of fields outside the spatial sciences, where concepts, software, and hardware with strong relationships to DE are being developed without referring to this term. It also presents a number of guiding criteria for future DE developments.

Tracing Forest Change through 40 Years on Two Continents with the BULC Algorithm and Google Earth Engine

NASA Astrophysics Data System (ADS)

Cardille, J. A.; Crowley, M.; Fortin, J. A.; Lee, J.; Perez, E.; Sleeter, B. M.; Thau, D.

2016-12-01

With the opening of the Landsat archive, researchers have a vast new data source teeming with imagery and potential. Beyond Landsat, data from other sensors is newly available as well: these include ALOS/PALSAR, Sentinel-1 and -2, MERIS, and many more. Google Earth Engine, developed to organize and provide analysis tools for these immense data sets, is an ideal platform for researchers trying to sift through huge image stacks. It offers nearly unlimited processing power and storage with a straightforward programming interface. Yet labeling land-cover change through time remains challenging given the current state of the art for interpreting remote sensing image sequences. Moreover, combining data from very different image platforms remains quite difficult. To address these challenges, we developed the BULC algorithm (Bayesian Updating of Land Cover), designed for the continuous updating of land-cover classifications through time in large data sets. The algorithm ingests data from any of the wide variety of earth-resources sensors; it maintains a running estimate of land-cover probabilities and the most probable class at all time points along a sequence of events. Here we compare BULC results from two study sites that witnessed considerable forest change in the last 40 years: the Pacific Northwest of the United States and the Mato Grosso region of Brazil. In Brazil, we incorporated rough classifications from more than 100 images of varying quality, mixing imagery from more than 10 different sensors. In the Pacific Northwest, we used BULC to identify forest changes due to logging and urbanization from 1973 to the present. Both regions had classification sequences that were better than many of the component days, effectively ignoring clouds and other unwanted noise while fusing the information contained on several platforms. As we leave remote sensing's data-poor era and enter a period with multiple looks at Earth's surface from multiple sensors over a short period of time, the BULC algorithm can help to sift through images of varying quality in Google Earth Engine to extract the most useful information for mapping the state and history of Earth's land cover.

Garver Google+ Hangout

NASA Image and Video Library

2013-05-31

NASA Deputy Administrator Lori Garver participates in a live "We The Geeks" Google+ Hangout hosted by the White House to talk about asteroids, Friday, May 31, 2013 at NASA Headquarters in Washington. An asteroid nearly three kilometers wide will pass by the Earth today at 3.6 million miles away. Garver is joined in the conversation by Bill Nye, Executive Director, Planetary Society; Ed Lu, former astronaut and CEO, B612 Foundation; Peter Diamandis, Co-Founder and Co-Chairman, Planetary Resources and Jose Luis Galache, Astronomer at the International Astronomical Unions's Minor Planet Center. Photo Credit: (NASA/Carla Cioffi)

3D Viewer Platform of Cloud Clustering Management System: Google Map 3D

NASA Astrophysics Data System (ADS)

Choi, Sung-Ja; Lee, Gang-Soo

The new management system of framework for cloud envrionemnt is needed by the platfrom of convergence according to computing environments of changes. A ISV and small business model is hard to adapt management system of platform which is offered from super business. This article suggest the clustering management system of cloud computing envirionments for ISV and a man of enterprise in small business model. It applies the 3D viewer adapt from map3D & earth of google. It is called 3DV_CCMS as expand the CCMS[1].

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

PubMed Central

Holl, Felix; Savory, David J.; Andrade-Pacheco, Ricardo; Gething, Peter W.; Bennett, Adam; Sturrock, Hugh J. W.

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. PMID:28953943

Reaching the Next Generation of College Students via Their Digital Devices.

NASA Astrophysics Data System (ADS)

Whitmeyer, S. J.; De Paor, D. G.; Bentley, C.

2015-12-01

Current college students attended school during a decade in which many school districts banned cellphones from the classroom or even from school grounds. These students are used to being told to put away their mobile devices and concentrate on traditional classroom activities such as watching PowerPoint presentations or calculating with pencil and paper. However, due to a combination of parental security concerns and recent education research, schools are rapidly changing policy and embracing mobile devices for ubiquitous learning opportunities inside and outside of the classroom. Consequently, many of the next generation of college students will have expectations of learning via mobile technology. We have developed a range of digital geology resources to aid mobile-based geoscience education at college level, including mapping on iPads and other tablets, "crowd-sourced" field projects, augmented reality-supported asynchronous field classes, 3D and 4D split-screen virtual reality tours, macroscopic and microscopic gigapixel imagery, 360° panoramas, assistive devices for inclusive field education, and game-style educational challenges. Class testing of virtual planetary tours shows modest short-term learning gains, but more work is needed to ensure long-term retention. Many of our resources rely on the Google Earth browser plug-in and application program interface (API). Because of security concerns, browser plug-ins in general are being phased out and the Google Earth API will not be supported in future browsers. However, a new plug-in-free API is promised by Google and an alternative open-source virtual globe called Cesium is undergoing rapid development. It already supports the main aspects of Keyhole Markup Language and has features of significant benefit to geoscience, including full support on mobile devices and sub-surface viewing and touring. The research team includes: Heather Almquist, Stephen Burgin, Cinzia Cervato, Filis Coba, Chloe Constants, Gene Cooper, Mladen Dordevic, Marissa Dudek, Brandon Fitzwater, Bridget Gomez, Tyler Hansen, Paul Karabinos, Terry Pavlis, Jen Piatek, Alan Pitts, Robin Rohrback, Bill Richards, Caroline Robinson, Jeff Rollins, Jeff Ryan, Ron Schott, Kristen St. John, and Barb Tewksbury. Supported by NSF DUE 1323419 and by Google Geo Curriculum Awards.

Data-Driven Geospatial Visual Analytics for Real-Time Urban Flooding Decision Support

NASA Astrophysics Data System (ADS)

Liu, Y.; Hill, D.; Rodriguez, A.; Marini, L.; Kooper, R.; Myers, J.; Wu, X.; Minsker, B. S.

2009-12-01

Urban flooding is responsible for the loss of life and property as well as the release of pathogens and other pollutants into the environment. Previous studies have shown that spatial distribution of intense rainfall significantly impacts the triggering and behavior of urban flooding. However, no general purpose tools yet exist for deriving rainfall data and rendering them in real-time at the resolution of hydrologic units used for analyzing urban flooding. This paper presents a new visual analytics system that derives and renders rainfall data from the NEXRAD weather radar system at the sewershed (i.e. urban hydrologic unit) scale in real-time for a Chicago stormwater management project. We introduce a lightweight Web 2.0 approach which takes advantages of scientific workflow management and publishing capabilities developed at NCSA (National Center for Supercomputing Applications), streaming data-aware semantic content management repository, web-based Google Earth/Map and time-aware KML (Keyhole Markup Language). A collection of polygon-based virtual sensors is created from the NEXRAD Level II data using spatial, temporal and thematic transformations at the sewershed level in order to produce persistent virtual rainfall data sources for the animation. Animated color-coded rainfall map in the sewershed can be played in real-time as a movie using time-aware KML inside the web browser-based Google Earth for visually analyzing the spatiotemporal patterns of the rainfall intensity in the sewershed. Such system provides valuable information for situational awareness and improved decision support during extreme storm events in an urban area. Our further work includes incorporating additional data (such as basement flooding events data) or physics-based predictive models that can be used for more integrated data-driven decision support.

Inspiring the Next Generation of Scientists: Building on 52 Years of Tradition in Diatom Research with Open-Source, Web-Based Collaboration Tools and Online Resources in a Field Course for High School Students

NASA Astrophysics Data System (ADS)

Howard, K. L.; Lee, S. S.

2015-12-01

Open-source, web-based forums and online resources can be used to develop a collaborative, active-learning approach for engaging and training students in the scientific process. We used the Diatoms of the United States website as an online resource for diatom taxonomy and developed a Google+ class community to serve as a platform for high school students to learn about research in diatom taxonomy, community ecology and diatom applications to the earth sciences. Ecology and Systematics of Diatoms is a field course that has been taught at the undergraduate and graduate levels at the Iowa Lakeside Lab field station for 52 years, beginning with the Diatom Clinic in 1963. Freshwater diatom education at Lakeside Lab has since evolved into a foundational training course attracting budding diatomists from all over the world, and has grown to include a week-long course for high school students. Successful since 2012, the high school course is now offered for college credit (University of Iowa), and covers methods of diatom specimen collection and preparation, microscopy, identification of diatom genera, diatom ecology, applications of diatom research, and an introduction to data analysis incorporating multivariate statistics (ordination) using the R statistical program, as well as primary scientific literature. During the 2015 course, students contributed to a Google+ class community where they posted images, data, and questions. The web-based platform allowed students to easily share information and to give and receive feedback from both peers and instructors. Students collaborated via the Google+ community and used the Diatoms of the United States website to develop a taxonomic reference for a field-based group research project, simulating how an actual diatom research program would develop a region or project-specific flora harmonized across analysts. Students investigated the taxonomy and ecology of diatom epiphytes on the green alga Cladophora from the littoral zone of West Lake Okoboji, Iowa. They found the epiphyte community went through a seasonal succession and developed hypotheses for the observed patterns by researching the ecology of diatoms in primary literature. These course activities may be used as a model for other field-based courses or educational programs in earth and environmental sciences.

Mapping for the masses: using free remote sensing data for disaster management

NASA Astrophysics Data System (ADS)

Teeuw, R.; McWilliam, N.; Morris, N.; Saunders, C.

2009-04-01

We examine the uses of free satellite imagery and Digital Elevation Models (DEMs) for disaster management, targeting three data sources: the United Nations Charter on Space and Disasters, Google Earth and internet-based satellite data archives, such as the Global Land Cover Facility (GLCF). The research has assessed SRTM and ASTER DEM data, Landsat TM/ETM+ and ASTER imagery, as well as utilising datasets and basic GIS operations available via Google Earth. As an aid to Disaster Risk Reduction, four sets of maps can be produced from satellite data: (i) Multiple Geohazards: areas prone to slope instability, coastal inundation and fluvial flooding; (ii) Vulnerability: population density, habitation types, land cover types and infrastructure; (iii) Disaster Risk: produced by combining severity scores from (i) and (ii); (iv) Reconstruction: zones of rock/sediment with construction uses; areas of woodland (for fuel/construction) water sources; transport routes; zones suitable for re-settlement. This set of Disaster Risk Reduction maps are ideal for regional (1:50,000 to 1:250,000 scale) planning for in low-income countries: more detailed assessments require relatively expensive high resolution satellite imagery or aerial photography, although Google Earth has a good track record for posting high-res imagery of disaster zones (e.g. the 2008 Burma storm surge). The Disaster Risk maps highlight areas of maximum risk to a region's emergency planners and decision makers, enabling various types of public education and other disaster mitigation measures. The Reconstruction map also helps to save lives, by facilitating disaster recovery. Many problems have been identified. Access to the UN Charter imagery is fine after a disaster, but very difficult if assessing pre-disaster indicators: the data supplied also tends to be pre-processed, when some relief agencies would prefer to have raw data. The limited and expensive internet access in many developing countries limits access to archives of free satellite data, such as the GLCF. Finally, data integration, spatial/temporal analysis and map production are all hindered by the high price of most GIS software, making the development of suitable open-source software a priority.

Landsat Based Woody Vegetation Loss Detection in Queensland, Australia Using the Google Earth Engine

NASA Astrophysics Data System (ADS)

Johansen, K.; Phinn, S. R.; Taylor, M.

2014-12-01

Land clearing detection and woody Foliage Projective Cover (FPC) monitoring at the state and national level in Australia has mainly been undertaken by state governments and the Terrestrial Ecosystem Research Network (TERN) because of the considerable expense, expertise, sustained duration of activities and staffing levels needed. Only recently have services become available, providing low budget, generalized access to change detection tools suited to this task. The objective of this research was to examine if a globally available service, Google Earth Engine Beta, could be used to predict woody vegetation loss with accuracies approaching the methods used by TERN and the government of the state of Queensland, Australia. Two change detection approaches were investigated using Landsat Thematic Mapper time series and the Google Earth Engine Application Programming Interface: (1) CART and Random Forest classifiers; and (2) a normalized time series of Foliage Projective Cover (FPC) and NDVI combined with a spectral index. The CART and Random Forest classifiers produced high user's and producer's mapping accuracies of clearing (77-92% and 54-77%, respectively) when detecting change within epochs for which training data were available, but extrapolation to epochs without training data reduced the mapping accuracies. The use of FPC and NDVI time series provided a more robust approach for calculation of a clearing probability, as it did not rely on training data but instead on the difference of the normalized FPC / NDVI mean and standard deviation of a single year at the change point in relation to the remaining time series. However, the FPC and NDVI time series approach represented a trade-off between user's and producer's accuracies. Both change detection approaches explored in this research were sensitive to ephemeral greening and drying of the landscape. However, the developed normalized FPC and NDVI time series approach can be tuned to provide automated alerts for large woody vegetation clearing events by selecting suitable thresholds to identify very likely clearing. This research provides a comprehensive foundation to build further capacity to use globally accessible, free, online image datasets and processing tools to accurately detect woody vegetation clearing in an automated and rapid manner.

Global Analysis of River Planform Change using the Google Earth Engine

NASA Astrophysics Data System (ADS)

Bryk, A.; Dietrich, W. E.; Gorelick, N.; Sargent, R.; Braudrick, C. A.

2014-12-01

Geomorphologists have historically tracked river dynamics using a combination of maps, aerial photographs, and the stratigraphic record. Although stratigraphic records can extend into deep time, maps and aerial photographs often confine our record of change to sparse measurements over the last ~80 years and in some cases much less time. For the first time Google's Earth Engine (GEE) cloud based platform allows researchers the means to analyze quantitatively the pattern and pace of river channel change over the last 30 years with high temporal resolution across the entire planet. The GEE provides an application programing interface (API) that enables quantitative analysis of various data sets including the entire Landsat L1T archive. This allows change detection for channels wider than about 150 m over 30 years of successive, georeferenced imagery. Qualitatively, it becomes immediately evident that the pace of channel morphodynamics for similar planforms varies by orders of magnitude across the planet and downstream along individual rivers. To quantify these rates of change and to explore their controls we have developed methods for differentiating channels from floodplain along large alluvial rivers. We introduce a new metric of morphodynamics: the ratio of eroded area to channel area per unit time, referred to as "M". We also keep track of depositional areas resulting from channel shifting. To date our quantitative analysis has focused on rivers in the Andean foreland. Our analysis shows channel bank erosion rates, M, varies by orders of magnitude for these rivers, from 0 to ~0.25 yr-1, yet these rivers have essentially identical curvature and sinuosity and are visually indistinguishable. By tracking both bank paths in time, we find that, for some meandering rivers, a significant fraction of new floodplain is produced through outer-bank accretion rather than point bar deposition. This process is perhaps more important in generating floodplain stratigraphy than previously recognized. These initial findings indicate a new set of quantitative observations will emerge to further test and advance morphodynamic theory. The Google Earth Engine offers the opportunity to explore river morphodynamics on an unprecedented scale and provides a powerful tool for addressing fundamental questions in river morphodynamics.

Virtual Globes, where we were, are and will be

NASA Astrophysics Data System (ADS)

Dehn, J.; Webley, P. W.; Worden, A. K.

2016-12-01

Ten years ago, Google Earth was new, and the first "Virtual Globes" session was held at AGU. Only a few of us realized the potential of this technology at the time, but the idea quickly caught on. At that time a virtual globe came in two flavors, first a complex GIS system that was utterly impenetrable for the public, or a more accessible version with limited functionality and layers that was available on a desktop computer with a good internet connection. Google Earth's use of the Keyhole Markup Language opened the door for scientists and the public to share data and visualizations across disciplines and revolutionized how everyone uses geographic data. In the following 10 years, KML became more advanced, virtual globes moved to mobile and handheld platforms, and the Google Earth engine allowed for more complex data sharing among scientists. Virtual globe images went from a rare commodity to being everywhere in our lives, from weather forecasts, in our cars, on our smart-phones and shape how we receive and process data. This is a fantastic tool for education and with newer technologies can reach the the remote corners of the world and developing countries. New and emerging technologies allow for augmented reality to be merged with the globes, and for real-time data integration with sensors built into mobile devices or add-ons. This presentation will follow the history of virtual globes in the geosciences, show how robust technologies can be used in the field and classroom today, and make some suggestions for the future.

Use of Google Earth to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments.

PubMed

Lozano-Fuentes, Saul; Elizondo-Quiroga, Darwin; Farfan-Ale, Jose Arturo; Loroño-Pino, Maria Alba; Garcia-Rejon, Julian; Gomez-Carro, Salvador; Lira-Zumbardo, Victor; Najera-Vazquez, Rosario; Fernandez-Salas, Ildefonso; Calderon-Martinez, Joaquin; Dominguez-Galera, Marco; Mis-Avila, Pedro; Morris, Natashia; Coleman, Michael; Moore, Chester G; Beaty, Barry J; Eisen, Lars

2008-09-01

Novel, inexpensive solutions are needed for improved management of vector-borne and other diseases in resource-poor environments. Emerging free software providing access to satellite imagery and simple editing tools (e.g. Google Earth) complement existing geographic information system (GIS) software and provide new opportunities for: (i) strengthening overall public health capacity through development of information for city infrastructures; and (ii) display of public health data directly on an image of the physical environment. We used freely accessible satellite imagery and a set of feature-making tools included in the software (allowing for production of polygons, lines and points) to generate information for city infrastructure and to display disease data in a dengue decision support system (DDSS) framework. Two cities in Mexico (Chetumal and Merida) were used to demonstrate that a basic representation of city infrastructure useful as a spatial backbone in a DDSS can be rapidly developed at minimal cost. Data layers generated included labelled polygons representing city blocks, lines representing streets, and points showing the locations of schools and health clinics. City blocks were colour-coded to show presence of dengue cases. The data layers were successfully imported in a format known as shapefile into a GIS software. The combination of Google Earth and free GIS software (e.g. HealthMapper, developed by WHO, and SIGEpi, developed by PAHO) has tremendous potential to strengthen overall public health capacity and facilitate decision support system approaches to prevention and control of vector-borne diseases in resource-poor environments.

Building Knowledge Graphs for NASA's Earth Science Enterprise

NASA Astrophysics Data System (ADS)

Zhang, J.; Lee, T. J.; Ramachandran, R.; Shi, R.; Bao, Q.; Gatlin, P. N.; Weigel, A. M.; Maskey, M.; Miller, J. J.

2016-12-01

Inspired by Google Knowledge Graph, we have been building a prototype Knowledge Graph for Earth scientists, connecting information and data in NASA's Earth science enterprise. Our primary goal is to advance the state-of-the-art NASA knowledge extraction capability by going beyond traditional catalog search and linking different distributed information (such as data, publications, services, tools and people). This will enable a more efficient pathway to knowledge discovery. While Google Knowledge Graph provides impressive semantic-search and aggregation capabilities, it is limited to search topics for general public. We use the similar knowledge graph approach to semantically link information gathered from a wide variety of sources within the NASA Earth Science enterprise. Our prototype serves as a proof of concept on the viability of building an operational "knowledge base" system for NASA Earth science. Information is pulled from structured sources (such as NASA CMR catalog, GCMD, and Climate and Forecast Conventions) and unstructured sources (such as research papers). Leveraging modern techniques of machine learning, information retrieval, and deep learning, we provide an integrated data mining and information discovery environment to help Earth scientists to use the best data, tools, methodologies, and models available to answer a hypothesis. Our knowledge graph would be able to answer questions like: Which articles discuss topics investigating similar hypotheses? How have these methods been tested for accuracy? Which approaches have been highly cited within the scientific community? What variables were used for this method and what datasets were used to represent them? What processing was necessary to use this data? These questions then lead researchers and citizen scientists to investigate the sources where data can be found, available user guides, information on how the data was acquired, and available tools and models to use with this data. As a proof of concept, we focus on a well-defined domain - Hurricane Science linking research articles and their findings, data, people and tools/services. Modern information retrieval, natural language processing machine learning and deep learning techniques are applied to build the knowledge network.

EarthScope Plate Boundary Observatory Data in the College Classroom (Invited)

NASA Astrophysics Data System (ADS)

Eriksson, S. C.; Olds, S. E.

2009-12-01

The Plate Boundary Observatory (PBO) is the geodetic component of the EarthScope project, designed to study the 3-D strain field across the active boundary zone between the Pacific and North American tectonics plates in the western United States. All PBO data are freely available to scientific and educational communities and have been incorporated into a variety of activities for college and university classrooms. UNAVCO Education and Outreach program staff have worked closely with faculty users, scientific researchers, and facility staff to create materials that are scientifically and technically accurate as well as useful to the classroom user. Availability of processed GPS data is not new to the geoscience community. However, PBO data staff have worked with education staff to deliver data that are readily accessible to educators. The UNAVCO Data for Educators webpage, incorporating an embedded Google Map with PBO GPS locations and providing current GPS time series plots and downloadable data, extends and updates the datasets available to our community. Google Earth allows the visualization GPS data with other types of datasets, e.g. LiDAR, while maintaining the self-contained and easy-to-use interface of UNAVCO’s Jules Verne Voyager map tools, which have multiple sets of geological and geophysical data. Curricular materials provide scaffolds for using EarthScope data in a variety of forms for different learning goals. Simple visualization of earthquake epicenters and locations of volcanoes can be used with velocity vectors to make simple deductions of plate boundary behaviors. Readily available time series plots provide opportunities for additional science skills, and there are web and paper-based support materials for downloading data, manipulating tables, and using plotting programs for processed GPS data. Scientists have provided contextual materials to explore the importance of these data in interpreting the structure and dynamics of the Earth. These data and their scientific context are now incorporated into the Active Earth Display developed by IRIS. Formal and informal evaluations during the past five years have provided useful data for revision and on-line implementation.

Assessment of the detectability of geo-hazards using Google Earth applied to the Three Parallel Rivers Area, Yunnan province of China

NASA Astrophysics Data System (ADS)

Voermans, Michiel; Mao, Zhun; Baartman, Jantiene EM; Stokes, Alexia

2017-04-01

Anthropogenic activities such as hydropower, mining and road construction in mountainous areas can induce and intensify mass wasting geo-hazards (e.g. landslides, gullies, rockslides). This represses local safety and socio-economic development, and endangers biodiversity at larger scale. Until today, data and knowledge to construct geo-hazard databases for further assessments are lacking. This applies in particular to countries with a recently emerged rapid economic growth, where there are no previous hazard documentations and where means to gain data from e.g. intensive fieldwork or VHR satellite imagery and DEM processing are lacking. Google Earth (GE, https://www.google.com/earth/) is a freely available and relatively simple virtual globe, map and geographical information program, which is potentially useful in detecting geo-hazards. This research aimed at (i) testing the capability of Google Earth to detect locations of geo-hazards and (ii) identifying factors affecting the diagnosing quality of the detection, including effects of geo-hazard dimensions, environs setting and professional background and effort of GE users. This was tested on nine geo-hazard sites following road segments in the Three Parallel Rivers Area in the Yunnan province of China, where geo-hazards are frequently occurring. Along each road site, the position and size of each geo-hazard was measured in situ. Next, independent diagnosers with varying professional experience (students, researchers, engineers etc.) were invited to detect geo-hazard occurrence along each of the eight sites via GE. Finally, the inventory and diagnostic data were compared to validate the objectives. Rates of detected geo-hazards from 30 diagnosers ranged from 10% to 48%. No strong correlations were found between the type and size of the geo-hazards and their detection rates. Also the years of expertise of the diagnosers proved not to make a difference, opposite to what may be expected. Meanwhile the amount of time spent by the diagnoser proved to be positively influencing the detectability. GE showed to be a useful tool in detecting mainly larger geo-hazards if diligently applied, and is therefore applicable to identify geo-hazard hotspots. The usability for further assessments such as sediment delivery estimations is questionable and further research should be carried out to give insight to its full potential.

Simulation of shoreline development in a groyne system, with a case study Sanur Bali beach

NASA Astrophysics Data System (ADS)

Gunawan, P. H.; Pudjaprasetya, S. R.

2018-03-01

The process of shoreline changes due to transport of sediment by littoral drift is studied in this paper. Pelnard-Considère is the commonly adopted model. This model is based on the principle of sediment conservation, without diffraction. In this research, we adopt the Pelnard-Considère equation with diffraction, and a numerical scheme based on the finite volume method is implemented. Shoreline development in a groyne system is then simulated. For a case study, the Sanur Bali Beach, Indonesia is considered, in which from Google Earth photos, the beach experiences changes of coastline caused by sediment trapped in a groyne system.

A method for vreating a three dimensional model from published geologic maps and cross sections

USGS Publications Warehouse

Walsh, Gregory J.

2009-01-01

This brief report presents a relatively inexpensive and rapid method for creating a 3D model of geology from published quadrangle-scale maps and cross sections using Google Earth and Google SketchUp software. An example from the Green Mountains of Vermont, USA, is used to illustrate the step by step methods used to create such a model. A second example is provided from the Jebel Saghro region of the Anti-Atlas Mountains of Morocco. The report was published to help enhance the public?s ability to use and visualize geologic map data.

The impact of geo-tagging on the photo industry and creating revenue streams

NASA Astrophysics Data System (ADS)

Richter, Rolf; Böge, Henning; Weckmann, Christoph; Schloen, Malte

2010-02-01

Internet geo and mapping services like Google Maps, Google Earth and Microsoft Bing Maps have reinvented the use of geographical information and have reached an enormous popularity. Besides that, location technologies like GPS have become affordable and are now being integrated in many camera phones. GPS is also available for standalone cameras as add on products or integrated in cameras. These developments are the enabler for new products for the photo industry or they enhance existing products. New commercial opportunities have been identified in the areas of photo hardware, internet/software and photo finishing.

The Chandra Source Catalog : Google Earth Interface

NASA Astrophysics Data System (ADS)

Glotfelty, Kenny; McLaughlin, W.; Evans, I.; Evans, J.; Anderson, C. S.; Bonaventura, N. R.; Davis, J. E.; Doe, S. M.; Fabbiano, G.; Galle, E. C.; Gibbs, D. G., II; Grier, J. D.; Hain, R.; Hall, D. M.; Harbo, P. N.; He, H.; Houck, J. C.; Karovska, M.; Kashyap, V. L.; Lauer, J.; McCollough, M. L.; McDowell, J. C.; Miller, J. B.; Mitschang, A. W.; Morgan, D. L.; Mossman, A. E.; Nichols, J. S.; Nowak, M. A.; Plummer, D. A.; Primini, F. A.; Refsdal, B. L.; Rots, A. R.; Siemiginowska, A. L.; Sundheim, B. A.; Tibbetts, M. S.; van Stone, D. W.; Winkelman, S. L.; Zografou, P.

2009-09-01

The Chandra Source Catalog (CSC) contains multi-resolution, exposure corrected, background subtracted, full-field images that are stored as individual FITS files and as three-color JPEG files. In this poster we discuss how we took these data and were able to, with relatively minimal effort, convert them for use with the Google Earth application in its ``Sky'' mode. We will highlight some of the challenges which include converting the data to the required Mercator projection, reworking the 3-color algorithm for pipeline processing, and ways to reduce the data volume through re-binning, using color-maps, and special Keyhole Markup Language (kml) tags to only load images on-demand. The result is a collection of some 11,000 3-color images that are available for all the individual observation in the CSC Release 1. We also have made available all ˜4000 Field-of-View outlines (with per-chip regions), which turns out are trivial to produce starting with a simple dmlist command. In the first week of release, approximately 40% of the images have been accessed at least once through some 50,000 individual web hits which have served over 4Gb of data to roughly 750 users in 60+ countries. We will also highlight some future directions we are exploring, including real-time catalog access to individual source properties and eventual access to file based products such as FITS images, spectra, and light-curves.

Harnessing Satellite Imageries in Feature Extraction Using Google Earth Pro

NASA Astrophysics Data System (ADS)

Fernandez, Sim Joseph; Milano, Alan

2016-07-01

Climate change has been a long-time concern worldwide. Impending flooding, for one, is among its unwanted consequences. The Phil-LiDAR 1 project of the Department of Science and Technology (DOST), Republic of the Philippines, has developed an early warning system in regards to flood hazards. The project utilizes the use of remote sensing technologies in determining the lives in probable dire danger by mapping and attributing building features using LiDAR dataset and satellite imageries. A free mapping software named Google Earth Pro (GEP) is used to load these satellite imageries as base maps. Geotagging of building features has been done so far with the use of handheld Global Positioning System (GPS). Alternatively, mapping and attribution of building features using GEP saves a substantial amount of resources such as manpower, time and budget. Accuracy-wise, geotagging by GEP is dependent on either the satellite imageries or orthophotograph images of half-meter resolution obtained during LiDAR acquisition and not on the GPS of three-meter accuracy. The attributed building features are overlain to the flood hazard map of Phil-LiDAR 1 in order to determine the exposed population. The building features as obtained from satellite imageries may not only be used in flood exposure assessment but may also be used in assessing other hazards and a number of other uses. Several other features may also be extracted from the satellite imageries.

A campus-based course in field geology

NASA Astrophysics Data System (ADS)

Richard, G. A.; Hanson, G. N.

2009-12-01

GEO 305: Field Geology offers students practical experience in the field and in the computer laboratory conducting geological field studies on the Stony Brook University campus. Computer laboratory exercises feature mapping techniques and field studies of glacial and environmental geology, and include geophysical and hydrological analysis, interpretation, and mapping. Participants learn to use direct measurement and mathematical techniques to compute the location and geometry of features and gain practical experience in representing raster imagery and vector geographic data as features on maps. Data collecting techniques in the field include the use of hand-held GPS devices, compasses, ground-penetrating radar, tape measures, pacing, and leveling devices. Assignments that utilize these skills and techniques include mapping campus geology with GPS, using Google Earth to explore our geologic context, data file management and ArcGIS, tape and compass mapping of woodland trails, pace and compass mapping of woodland trails, measuring elevation differences on a hillside, measuring geologic sections and cores, drilling through glacial deposits, using ground penetrating radar on glaciotectonic topography, mapping the local water table, and the identification and mapping of boulders. Two three-hour sessions are offered per week, apportioned as needed between lecture; discussion; guided hands-on instruction in geospatial and other software such as ArcGIS, Google Earth, spreadsheets, and custom modules such as an arc intersection calculator; outdoor data collection and mapping; and writing of illustrated reports.

Relevancy 101

NASA Technical Reports Server (NTRS)

Lynnes, Chris; Newman, Doug

2016-01-01

Where we present an overview on why relevancy is a problem, how important it is and how we can improve it. The topic of relevancy is becoming increasingly important in earth data discovery as our audience is tuned to the accuracy of standard search engines like Google.

Estimating Water Levels with Google Earth Engine

NASA Astrophysics Data System (ADS)

Lucero, E.; Russo, T. A.; Zentner, M.; May, J.; Nguy-Robertson, A. L.

2016-12-01

Reservoirs serve multiple functions and are vital for storage, electricity generation, and flood control. For many areas, traditional ground-based reservoir measurements may not be available or data dissemination may be problematic. Consistent monitoring of reservoir levels in data-poor areas can be achieved through remote sensing, providing information to researchers and the international community. Estimates of trends and relative reservoir volume can be used to identify water supply vulnerability, anticipate low power generation, and predict flood risk. Image processing with automated cloud computing provides opportunities to study multiple geographic areas in near real-time. We demonstrate the prediction capability of a cloud environment for identifying water trends at reservoirs in the US, and then apply the method to data-poor areas in North Korea, Iran, Azerbaijan, Zambia, and India. The Google Earth Engine cloud platform hosts remote sensing data and can be used to automate reservoir level estimation with multispectral imagery. We combine automated cloud-based analysis from Landsat image classification to identify reservoir surface area trends and radar altimetry to identify reservoir level trends. The study estimates water level trends using three years of data from four domestic reservoirs to validate the remote sensing method, and five foreign reservoirs to demonstrate the method application. We report correlations between ground-based reservoir level measurements in the US and our remote sensing methods, and correlations between the cloud analysis and altimetry data for reservoirs in data-poor areas. The availability of regular satellite imagery and an automated, near real-time application method provides the necessary datasets for further temporal analysis, reservoir modeling, and flood forecasting. All statements of fact, analysis, or opinion are those of the author and do not reflect the official policy or position of the Department of Defense or any of its components or the U.S. Government

Using Cloud-based Storage Technologies for Earth Science Data

NASA Astrophysics Data System (ADS)

Michaelis, A.; Readey, J.; Votava, P.

2016-12-01

Cloud based infrastructure may offer several key benefits of scalability, built in redundancy and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and software systems developed for NASA data repositories were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Object storage services are provided through all the leading public (Amazon Web Service, Microsoft Azure, Google Cloud, etc.) and private (Open Stack) clouds, and may provide a more cost-effective means of storing large data collections online. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows superior performance for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.

Analyst Performance Measures. Volume 1: Persistent Surveillance Data Processing, Storage and Retrieval

DTIC Science & Technology

2011-09-01

solutions to address these important challenges . The Air Force is seeking innovative architectures to process and store massive data sets in a flexible...Google Earth, the Video LAN Client ( VLC ) media player, and the Environmental Systems Research Institute corporation‘s (ESRI) ArcGIS product — to...Earth, Quantum GIS, VLC Media Player, NASA WorldWind, ESRI ArcGIS and many others. Open source GIS and media visualization software can also be

A New More Accurate Calibration for TIMED/GUVI

NASA Astrophysics Data System (ADS)

Schaefer, R. K.; Aiello, J.; Wolven, B. C.; Paxton, L. J.; Romeo, G.; Zhang, Y.

2017-12-01

The Global UltraViolet Imager (GUVI - http://guvi.jhuapl.edu) on NASA's TIMED spacecraft has the longest continuous set of observations of the Earth's ionosphere and thermosphere, spanning more than one solar cycle (2001-2017). As such, it represents an important dataset for understanding the dynamics of the Ionosphere-Thermosphere system. The entire dataset has been reprocessed and released as a new version (13) of GUVI data products. This is a complete re-examination of the calibration elements, including better calibrated radiances, better geolocation, and better background subtraction. Details can be found on the GUVI website: http://guvitimed.jhuapl.edu/guvi-Calib_Prod The radiances (except for the LBH long band) in version 13 are within 10% of the original archival radiances and so most of the derived products are little changed from their original versions. The LBH long band was redefined in on-board instrument color tables on Nov., 2, 2004 to better limit contamination from Nitric Oxide emission but this was not updated in ground processing until now. Version 13 LBH Long has 19% smaller radiances than the old calibrated products for post 11/2/2004 data. GUVI auroral products are the only ones that use LBHL - (LBH long)/(LBH short) is used to gauge the amount of intervening oxygen absorption. We will show several examples of the difference between new and old auroral products. Overall version 13 represents a big improvement in the calibration, geolocation, and background of the GUVI UV data products, allowing for the cleanest UV data for analysis of the ionosphere-thermosphere-aurora. An updated "Using GUVI Data Tutorial" will be available from the GUVI webpage to help you navigate to the data you need. Data products are displayed as daily summary and Google Earth files that can be browsed through the Cesium tool on the GUVI website or the image files can be downloaded and viewed through the Google Earth app. The image below shows gridded 135.6 nm radiances from March 27, 2003 displayed in Google Earth.

Tracing Forest Change through 40 Years on Two Continents with the BULC Algorithm and Google Earth Engine

NASA Astrophysics Data System (ADS)

Cardille, J. A.

2015-12-01

With the opening of the Landsat archive, researchers have a vast new data source teeming with imagery and potential. Beyond Landsat, data from other sensors is newly available as well: these include ALOS/PALSAR, Sentinel-1 and -2, MERIS, and many more. Google Earth Engine, developed to organize and provide analysis tools for these immense data sets, is an ideal platform for researchers trying to sift through huge image stacks. It offers nearly unlimited processing power and storage with a straightforward programming interface. Yet labeling forest change through time remains challenging given the current state of the art for interpreting remote sensing image sequences. Moreover, combining data from very different image platforms remains quite difficult. To address these challenges, we developed the BULC algorithm (Bayesian Updating of Land Cover), designed for the continuous updating of land-cover classifications through time in large data sets. The algorithm ingests data from any of the wide variety of earth-resources sensors; it maintains a running estimate of land-cover probabilities and the most probable class at all time points along a sequence of events. Here we compare BULC results from two study sites that witnessed considerable forest change in the last 40 years: the Pacific Northwest of the United States and the Mato Grosso region of Brazil. In Brazil, we incorporated rough classifications from more than 100 images of varying quality, mixing imagery from more than 10 different sensors. In the Pacific Northwest, we used BULC to identify forest changes due to logging and urbanization from 1973 to the present. Both regions had classification sequences that were better than many of the component days, effectively ignoring clouds and other unwanted signal while fusing the information contained on several platforms. As we leave remote sensing's data-poor era and enter a period with multiple looks at Earth's surface from multiple sensors over a short period of time, this algorithm may help to sift through images of varying quality in Google Earth Engine to extract the most useful information for mapping.

Large Scale Crop Mapping in Ukraine Using Google Earth Engine

NASA Astrophysics Data System (ADS)

Shelestov, A.; Lavreniuk, M. S.; Kussul, N.

2016-12-01

There are no globally available high resolution satellite-derived crop specific maps at present. Only coarse-resolution imagery (> 250 m spatial resolution) has been utilized to derive global cropland extent. In 2016 we are going to carry out a country level demonstration of Sentinel-2 use for crop classification in Ukraine within the ESA Sen2-Agri project. But optical imagery can be contaminated by cloud cover that makes it difficult to acquire imagery in an optimal time range to discriminate certain crops. Due to the Copernicus program since 2015, a lot of Sentinel-1 SAR data at high spatial resolution is available for free for Ukraine. It allows us to use the time series of SAR data for crop classification. Our experiment for one administrative region in 2015 showed much higher crop classification accuracy with SAR data than with optical only time series [1, 2]. Therefore, in 2016 within the Google Earth Engine Research Award we use SAR data together with optical ones for large area crop mapping (entire territory of Ukraine) using cloud computing capabilities available at Google Earth Engine (GEE). This study compares different classification methods for crop mapping for the whole territory of Ukraine using data and algorithms from GEE. Classification performance assessed using overall classification accuracy, Kappa coefficients, and user's and producer's accuracies. Also, crop areas from derived classification maps compared to the official statistics [3]. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297. N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "The use of satellite SAR imagery to crop classification in Ukraine within JECAM project," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1497-1500, 13-18 July 2014, Quebec City, Canada. F.J. Gallego, N. Kussul, S. Skakun, O. Kravchenko, A. Shelestov, O. Kussul, "Efficiency assessment of using satellite data for crop area estimation in Ukraine," International Journal of Applied Earth Observation and Geoinformation vol. 29, pp. 22-30, 2014.

Using Google Earth to Explore Multiple Data Sets and Plate Tectonic Concepts

NASA Astrophysics Data System (ADS)

Goodell, L. P.

2015-12-01

Google Earth (GE) offers an engaging and dynamic environment for exploration of earth science data. While GIS software offers higher-level analytical capability, it comes with a steep learning curve and complex interface that is not easy for the novice, and in many cases the instructor, to negotiate. In contrast, the intuitive interface of GE makes it easy for students to quickly become proficient in manipulating the globe and independently exploring relationships between multiple data sets at a wide range of scales. Inquiry-based, data-rich exercises have been developed for both introductory and upper-level activities including: exploration of plate boundary characteristics and relative motion across plate boundaries; determination and comparison of short-term and long-term average plate velocities; crustal strain analysis (modeled after the UNAVCO activity); and determining earthquake epicenters, body-wave magnitudes, and focal plane solutions. Used successfully in undergraduate course settings, for TA training and for professional development programs for middle and high school teachers, the exercises use the following GE data sets (with sources) that have been collected/compiled by the author and are freely available for non-commercial use: 1) tectonic plate boundaries and plate names (Bird, 2003 model); 2) real-time earthquakes (USGS); 3) 30 years of M>=5.0 earthquakes, plotted by depth (USGS); 4) seafloor age (Mueller et al., 1997, 2008); 5) location and age data for hot spot tracks (published literature); 6) Holocene volcanoes (Smithsonian Global Volcanism Program); 7) GPS station locations with links to times series (JPL, NASA, UNAVCO); 8) short-term motion vectors derived from GPS times series; 9) long-term average motion vectors derived from plate motion models (UNAVCO plate motion calculator); 10) earthquake data sets consisting of seismic station locations and links to relevant seismograms (Rapid Earthquake Viewer, USC/IRIS/DELESE).

Measuring river from the cloud - River width algorithm development on Google Earth Engine

NASA Astrophysics Data System (ADS)

Yang, X.; Pavelsky, T.; Allen, G. H.; Donchyts, G.

2017-12-01

Rivers are some of the most dynamic features of the terrestrial land surface. They help distribute freshwater, nutrients, sediment, and they are also responsible for some of the greatest natural hazards. Despite their importance, our understanding of river behavior is limited at the global scale, in part because we do not have a river observational dataset that spans both time and space. Remote sensing data represent a rich, largely untapped resource for observing river dynamics. In particular, publicly accessible archives of satellite optical imagery, which date back to the 1970s, can be used to study the planview morphodynamics of rivers at the global scale. Here we present an image processing algorithm developed using the Google Earth Engine cloud-based platform, that can automatically extracts river centerlines and widths from Landsat 5, 7, and 8 scenes at 30 m resolution. Our algorithm makes use of the latest monthly global surface water history dataset and an existing Global River Width from Landsat (GRWL) dataset to efficiently extract river masks from each Landsat scene. Then a combination of distance transform and skeletonization techniques are used to extract river centerlines. Finally, our algorithm calculates wetted river width at each centerline pixel perpendicular to its local centerline direction. We validated this algorithm using in situ data estimated from 16 USGS gauge stations (N=1781). We find that 92% of the width differences are within 60 m (i.e. the minimum length of 2 Landsat pixels). Leveraging Earth Engine's infrastructure of collocated data and processing power, our goal is to use this algorithm to reconstruct the morphodynamic history of rivers globally by processing over 100,000 Landsat 5 scenes, covering from 1984 to 2013.

EarthCache as a Tool to Promote Earth-Science in Public School Classrooms

NASA Astrophysics Data System (ADS)

Gochis, E. E.; Rose, W. I.; Klawiter, M.; Vye, E. C.; Engelmann, C. A.

2011-12-01

Geoscientists often find it difficult to bridge the gap in communication between university research and what is learned in the public schools. Today's schools operate in a high stakes environment that only allow instruction based on State and National Earth Science curriculum standards. These standards are often unknown by academics or are written in a style that obfuscates the transfer of emerging scientific research to students in the classroom. Earth Science teachers are in an ideal position to make this link because they have a background in science as well as a solid understanding of the required curriculum standards for their grade and the pedagogical expertise to pass on new information to their students. As part of the Michigan Teacher Excellence Program (MiTEP), teachers from Grand Rapids, Kalamazoo, and Jackson school districts participate in 2 week field courses with Michigan Tech University to learn from earth science experts about how the earth works. This course connects Earth Science Literacy Principles' Big Ideas and common student misconceptions with standards-based education. During the 2011 field course, we developed and began to implement a three-phase EarthCache model that will provide a geospatial interactive medium for teachers to translate the material they learn in the field to the students in their standards based classrooms. MiTEP participants use GPS and Google Earth to navigate to Michigan sites of geo-significance. At each location academic experts aide participants in making scientific observations about the locations' geologic features, and "reading the rocks" methodology to interpret the area's geologic history. The participants are then expected to develop their own EarthCache site to be used as pedagogical tool bridging the gap between standards-based classroom learning, contemporary research and unique outdoor field experiences. The final phase supports teachers in integrating inquiry based, higher-level learning student activities to EarthCache sites near their own urban communities, or in regional areas such as nature preserves and National Parks. By working together, MiTEP participants are developing a network of regional EarthCache sites and shared lesson plans which explore places that are meaningful to students while simultaneously connecting them to geologic concepts they are learning in school. We believe that the MiTEP EarthCaching model will help participants emerge as leaders of inquiry style, and virtual place-based educators within their districts.

Creating User-Friendly Tools for Data Analysis and Visualization in K-12 Classrooms: A Fortran Dinosaur Meets Generation Y

NASA Technical Reports Server (NTRS)

Chambers, L. H.; Chaudhury, S.; Page, M. T.; Lankey, A. J.; Doughty, J.; Kern, Steven; Rogerson, Tina M.

2008-01-01

During the summer of 2007, as part of the second year of a NASA-funded project in partnership with Christopher Newport University called SPHERE (Students as Professionals Helping Educators Research the Earth), a group of undergraduate students spent 8 weeks in a research internship at or near NASA Langley Research Center. Three students from this group formed the Clouds group along with a NASA mentor (Chambers), and the brief addition of a local high school student fulfilling a mentorship requirement. The Clouds group was given the task of exploring and analyzing ground-based cloud observations obtained by K-12 students as part of the Students' Cloud Observations On-Line (S'COOL) Project, and the corresponding satellite data. This project began in 1997. The primary analysis tools developed for it were in FORTRAN, a computer language none of the students were familiar with. While they persevered through computer challenges and picky syntax, it eventually became obvious that this was not the most fruitful approach for a project aimed at motivating K-12 students to do their own data analysis. Thus, about halfway through the summer the group shifted its focus to more modern data analysis and visualization tools, namely spreadsheets and Google(tm) Earth. The result of their efforts, so far, is two different Excel spreadsheets and a Google(tm) Earth file. The spreadsheets are set up to allow participating classrooms to paste in a particular dataset of interest, using the standard S'COOL format, and easily perform a variety of analyses and comparisons of the ground cloud observation reports and their correspondence with the satellite data. This includes summarizing cloud occurrence and cloud cover statistics, and comparing cloud cover measurements from the two points of view. A visual classification tool is also provided to compare the cloud levels reported from the two viewpoints. This provides a statistical counterpart to the existing S'COOL data visualization tool, which is used for individual ground-to-satellite correspondences. The Google(tm) Earth file contains a set of placemarks and ground overlays to show participating students the area around their school that the satellite is measuring. This approach will be automated and made interactive by the S'COOL database expert and will also be used to help refine the latitude/longitude location of the participating schools. Once complete, these new data analysis tools will be posted on the S'COOL website for use by the project participants in schools around the US and the world.

A Virtual Tour of the 1868 Hayward Earthquake in Google EarthTM

NASA Astrophysics Data System (ADS)

Lackey, H. G.; Blair, J. L.; Boatwright, J.; Brocher, T.

2007-12-01

The 1868 Hayward earthquake has been overshadowed by the subsequent 1906 San Francisco earthquake that destroyed much of San Francisco. Nonetheless, a modern recurrence of the 1868 earthquake would cause widespread damage to the densely populated Bay Area, particularly in the east Bay communities that have grown up virtually on top of the Hayward fault. Our concern is heightened by paleoseismic studies suggesting that the recurrence interval for the past five earthquakes on the southern Hayward fault is 140 to 170 years. Our objective is to build an educational web site that illustrates the cause and effect of the 1868 earthquake drawing on scientific and historic information. We will use Google EarthTM software to visually illustrate complex scientific concepts in a way that is understandable to a non-scientific audience. This web site will lead the viewer from a regional summary of the plate tectonics and faulting system of western North America, to more specific information about the 1868 Hayward earthquake itself. Text and Google EarthTM layers will include modeled shaking of the earthquake, relocations of historic photographs, reconstruction of damaged buildings as 3-D models, and additional scientific data that may come from the many scientific studies conducted for the 140th anniversary of the event. Earthquake engineering concerns will be stressed, including population density, vulnerable infrastructure, and lifelines. We will also present detailed maps of the Hayward fault, measurements of fault creep, and geologic evidence of its recurrence. Understanding the science behind earthquake hazards is an important step in preparing for the next significant earthquake. We hope to communicate to the public and students of all ages, through visualizations, not only the cause and effect of the 1868 earthquake, but also modern seismic hazards of the San Francisco Bay region.

The extent of forest in dryland biomes.

PubMed

Bastin, Jean-François; Berrahmouni, Nora; Grainger, Alan; Maniatis, Danae; Mollicone, Danilo; Moore, Rebecca; Patriarca, Chiara; Picard, Nicolas; Sparrow, Ben; Abraham, Elena Maria; Aloui, Kamel; Atesoglu, Ayhan; Attore, Fabio; Bassüllü, Çağlar; Bey, Adia; Garzuglia, Monica; García-Montero, Luis G; Groot, Nikée; Guerin, Greg; Laestadius, Lars; Lowe, Andrew J; Mamane, Bako; Marchi, Giulio; Patterson, Paul; Rezende, Marcelo; Ricci, Stefano; Salcedo, Ignacio; Diaz, Alfonso Sanchez-Paus; Stolle, Fred; Surappaeva, Venera; Castro, Rene

2017-05-12

Dryland biomes cover two-fifths of Earth's land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than 210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high temporal resolution, which are available through the Google Earth platform. We show that in 2015, 1327 million hectares of drylands had more than 10% tree-cover, and 1079 million hectares comprised forest. Our estimate is 40 to 47% higher than previous estimates, corresponding to 467 million hectares of forest that have never been reported before. This increases current estimates of global forest cover by at least 9%. Copyright © 2017, American Association for the Advancement of Science.

Assessing and Minimizing Adversarial Risk in a Nuclear Material Transportation Network

DTIC Science & Technology

2013-09-01

0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave Blank) 2. REPORT DATE 09-27-2013 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND...U.S. as of July 2013. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Figure A.1 Google Earth routing from Areva to Arkansas Nuclear...Uranium ore is mined or removed from the earth in a leaching process. 2. Conversion (1). Triuranium octoxide (U3O8, “yellowcake”) is converted into ura

Interactive Mapping of the Planets: An Online Activity Using the Google Earth Platform

NASA Astrophysics Data System (ADS)

Osinski, G. R.; Gilbert, A.; Harrison, T. N.; Mader, M. M.; Shankar, B.; Tornabene, L. L.

2013-12-01

With funding from the Natural Sciences and Engineering Research Council of Canada's PromoScience program and support from the Department of Earth Sciences at The University of Western Ontario, the Centre for Planetary Science and Exploration (CPSX) has developed a new web-based initiative called Interactive Mapping of the Planets (IMAPS). Additional components include in person school visits to deliver inquiry-based workshops, week-long summer camps, and pre-prepared impact rock lending kits, all framed around the IMAPS activity. IMAPS will is now in beta testing mode and will be demonstrated in this session. The general objective of the online activity is for participants to plan and design a rover mission to Mars based on a given mission goal - e.g., to find evidence for past water flow. The activity begins with participants receiving image-analysis training to learn about the different landforms on Mars and which ones are potentially caused by water flow. They then need to pass a short test to show they can consistently identify Martian landforms. From there, the participants choose a landing site and plan a traverse - utilizing the free Google Earth plug-in - and taking into account factors such as hazards and their sites of interest. A mission control blog will provide updates on the status of their mission and a 'choose your rover' option provides the opportunity to unlock more advanced rovers by collaborating with other scientists and rating their missions. Indeed, evaluation of missions will be done using a crowd-sourcing method. In addition to being fully accessible online, CPSX will also target primary- and secondary-school grades in which astronomy and space science is taught. Teachers in K-12 classrooms will be able to sign-up for the activity ahead of time in order to receive a workshop package, which will guide them on how to use the IMAPS online activity with their class. Teachers will be able to set up groups for their classroom so that they can evaluate their students based on pre-determined criteria. The IMAPS activities are developed in partnerships with the Department of Earth Sciences at Western University, Sports Western, the Thames Valley District School Board, and Dimentians Web Marketing and Design. We are continually looking for new collaborators to help design or test our inquiry- and web-based activities, provide feedback on our programs, or volunteer with us. Please contact cpsxoutreach@uwo.ca if you are interested.

A Web-Based Earth-Systems Knowledge Portal and Collaboration Platform

NASA Astrophysics Data System (ADS)

D'Agnese, F. A.; Turner, A. K.

2010-12-01

In support of complex water-resource sustainability projects in the Great Basin region of the United States, Earth Knowledge, Inc. has developed several web-based data management and analysis platforms that have been used by its scientists, clients, and public to facilitate information exchanges, collaborations, and decision making. These platforms support accurate water-resource decision-making by combining second-generation internet (Web 2.0) technologies with traditional 2D GIS and web-based 2D and 3D mapping systems such as Google Maps, and Google Earth. Most data management and analysis systems use traditional software systems to address the data needs and usage behavior of the scientific community. In contrast, these platforms employ more accessible open-source and “off-the-shelf” consumer-oriented, hosted web-services. They exploit familiar software tools using industry standard protocols, formats, and APIs to discover, process, fuse, and visualize earth, engineering, and social science datasets. Thus, they respond to the information needs and web-interface expectations of both subject-matter experts and the public. Because the platforms continue to gather and store all the contributions of their broad-spectrum of users, each new assessment leverages the data, information, and expertise derived from previous investigations. In the last year, Earth Knowledge completed a conceptual system design and feasibility study for a platform, which has a Knowledge Portal providing access to users wishing to retrieve information or knowledge developed by the science enterprise and a Collaboration Environment Module, a framework that links the user-access functions to a Technical Core supporting technical and scientific analyses including Data Management, Analysis and Modeling, and Decision Management, and to essential system administrative functions within an Administrative Module. The over-riding technical challenge is the design and development of a single technical platform that is accessed through a flexible series of knowledge portal and collaboration environment styles reflecting the information needs and user expectations of a diverse community of users. Recent investigations have defined the information needs and expectations of the major end-users and also have reviewed and assessed a wide variety of modern web-based technologies. Combining these efforts produced design specifications and recommendations for the selection and integration of web- and client-based tools. When fully developed, the resulting platform will: -Support new, advanced information systems and decision environments that take full advantage of multiple data sources and platforms; -Provide a distribution network tailored to the timely delivery of products to a broad range of users that are needed to support applications in disaster management, resource management, energy, and urban sustainability; -Establish new integrated multiple-user requirements and knowledge databases that support researchers and promote infusion of successful technologies into existing processes; and -Develop new decision support strategies and presentation methodologies for applied earth science applications to reduce risk, cost, and time.

A Web-Based Information System for Field Data Management

NASA Astrophysics Data System (ADS)

Weng, Y. H.; Sun, F. S.

2014-12-01

A web-based field data management system has been designed and developed to allow field geologists to store, organize, manage, and share field data online. System requirements were analyzed and clearly defined first regarding what data are to be stored, who the potential users are, and what system functions are needed in order to deliver the right data in the right way to the right user. A 3-tiered architecture was adopted to create this secure, scalable system that consists of a web browser at the front end while a database at the back end and a functional logic server in the middle. Specifically, HTML, CSS, and JavaScript were used to implement the user interface in the front-end tier, the Apache web server runs PHP scripts, and MySQL to server is used for the back-end database. The system accepts various types of field information, including image, audio, video, numeric, and text. It allows users to select data and populate them on either Google Earth or Google Maps for the examination of the spatial relations. It also makes the sharing of field data easy by converting them into XML format that is both human-readable and machine-readable, and thus ready for reuse.

Using Google Earth for Submarine Operations at Pavilion Lake

NASA Astrophysics Data System (ADS)

Deans, M. C.; Lees, D. S.; Fong, T.; Lim, D. S.

2009-12-01

During the July 2009 Pavilion Lake field test, we supported submarine "flight" operations using Google Earth. The Intelligent Robotics Group at NASA Ames has experience with ground data systems for NASA missions, earth analog field tests, disaster response, and the Gigapan camera system. Leveraging this expertise and existing software, we put together a set of tools to support sub tracking and mapping, called the "Surface Data System." This system supports flight planning, real time flight operations, and post-flight analysis. For planning, we make overlays of the regional bedrock geology, sonar bathymetry, and sonar backscatter maps that show geology, depth, and structure of the bottom. Placemarks show the mooring locations for start and end points. Flight plans are shown as polylines with icons for waypoints. Flight tracks and imagery from previous field seasons are embedded in the map for planning follow-on activities. These data provide context for flight planning. During flights, sub position is updated every 5 seconds from the nav computer on the chase boat. We periodically update tracking KML files and refresh them with network links. A sub icon shows current location of the sub. A compass rose shows bearings to indicate heading to the next waypoint. A "Science Stenographer" listens on the voice loop and transcribes significant observations in real time. Observations called up to the surface immediately appear on the map as icons with date, time, position, and what was said. After each flight, the science back room immediately has the flight track and georeferenced notes from the pilots. We add additional information in post-processing. The submarines record video continuously, with "event" timestamps marked by the pilot. We cross-correlate the event timestamps with position logs to geolocate events and put a preview image and compressed video clip into the map. Animated flight tracks are also generated, showing timestamped position and providing timelapse playback of the flight. Neogeography tools are increasing in popularity and offer an excellent platform for geoinformatics. The scientists on the team are already familiar with Google Earth, eliminating up-front training on new tools. The flight maps and archived data are available immediately and in a usable format. Google Earth provides lots of measurement tools, annotation tools, and other built-in functions that we can use to create and analyze the map. All of this information is saved to a shared filesystem so that everyone on the team has access to all of the same map data. After the field season, the map data will be used by the team to analyse and correlate information from across the lake and across different flights to support their research, and to plan next year's activities.

Power Plants Likely Covered by the Mercury and Air Toxics Standards (MATS)

EPA Pesticide Factsheets

The U.S. Environmental Protection Agency (EPA) has proposed Mercury and Air Toxics Standards (MATS) for power plants to limit mercury, acid gases and other toxic pollution from power plants. Using Google Earth, this page locates power plants in your state.

A Geospatial Scavenger Hunt

ERIC Educational Resources Information Center

Martinez, Adriana E.; Williams, Nikki A.; Metoyer, Sandra K.; Morris, Jennifer N.; Berhane, Stephen A.

2009-01-01

With the use of technology such as Global Positioning System (GPS) units and Google Earth for a simple-machine scavenger hunt, you will transform a standard identification activity into an exciting learning experience that motivates students, incorporates practical skills in technology, and enhances students' spatial-thinking skills. In the…

Kinematics with the assistance of smartphones: Measuring data via GPS - Visualizing data with Google Earth

NASA Astrophysics Data System (ADS)

Gabriel, Patrik; Backhaus, Udo

2013-04-01

Nearly every smartphone is now GPS capable. The widespread use of GPS navigation has developed alongside less expensive hardware and user-friendly software interfaces, which may help to bring scientific research and teaching closer to real life.

Teaching Genocide through GIS: A Transformative Approach

ERIC Educational Resources Information Center

Fitchett, Paul G.; Good, Amy J.

2012-01-01

The utilization of Geographical Information Systems (GIS) and geobrowsers (Google Earth) have become increasingly prevalent in the study of genocide. These applications offer teachers and students the opportunity to analyze historical and contemporary genocidal acts from a critical geographic perspective in which the confluence of historical…

Fuzzy B-spline optimization for urban slum three-dimensional reconstruction using ENVISAT satellite data

NASA Astrophysics Data System (ADS)

Marghany, Maged

2014-06-01

A critical challenges in urban aeras is slums. In fact, they are considered a source of crime and disease due to poor-quality housing, unsanitary conditions, poor infrastructures and occupancy security. The poor in the dense urban slums are the most vulnerable to infection due to (i) inadequate and restricted access to safety, drinking water and sufficient quantities of water for personal hygiene; (ii) the lack of removal and treatment of excreta; and (iii) the lack of removal of solid waste. This study aims to investigate the capability of ENVISAT ASAR satellite and Google Earth data for three-dimensional (3-D) slum urban reconstruction in developed countries such as Egypt. The main objective of this work is to utilize some 3-D automatic detection algorithm for urban slum in ENVISAT ASAR and Google Erath images were acquired in Cairo, Egypt using Fuzzy B-spline algorithm. The results show that the fuzzy algorithm is the best indicator for chaotic urban slum as it can discriminate between them from its surrounding environment. The combination of Fuzzy and B-spline then used to reconstruct 3-D of urban slum. The results show that urban slums, road network, and infrastructures are perfectly discriminated. It can therefore be concluded that the fuzzy algorithm is an appropriate algorithm for chaotic urban slum automatic detection in ENVSIAT ASAR and Google Earth data.

KML Super Overlay to WMS Translator

NASA Technical Reports Server (NTRS)

Plesea, Lucian

2007-01-01

This translator is a server-based application that automatically generates KML super overlay configuration files required by Google Earth for map data access via the Open Geospatial Consortium WMS (Web Map Service) standard. The translator uses a set of URL parameters that mirror the WMS parameters as much as possible, and it also can generate a super overlay subdivision of any given area that is only loaded when needed, enabling very large areas of coverage at very high resolutions. It can make almost any dataset available as a WMS service visible and usable in any KML application, without the need to reformat the data.

Community-Based Services that Facilitate Interoperability and Intercomparison of Precipitation Datasets from Multiple Sources

NASA Technical Reports Server (NTRS)

Liu, Zhong; Kempler, Steven; Teng, William; Leptoukh, Gregory; Ostrenga, Dana

2010-01-01

Over the past 12 years, large volumes of precipitation data have been generated from space-based observatories (e.g., TRMM), merging of data products (e.g., gridded 3B42), models (e.g., GMAO), climatologies (e.g., Chang SSM/I derived rain indices), field campaigns, and ground-based measuring stations. The science research, applications, and education communities have greatly benefited from the unrestricted availability of these data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) and, in particular, the services tailored toward precipitation data access and usability. In addition, tools and services that are responsive to the expressed evolving needs of the precipitation data user communities have been developed at the Precipitation Data and Information Services Center (PDISC) (http://disc.gsfc.nasa.gov/precipitation or google NASA PDISC), located at the GES DISC, to provide users with quick data exploration and access capabilities. In recent years, data management and access services have become increasingly sophisticated, such that they now afford researchers, particularly those interested in multi-data set science analysis and/or data validation, the ability to homogenize data sets, in order to apply multi-variant, comparison, and evaluation functions. Included in these services is the ability to capture data quality and data provenance. These interoperability services can be directly applied to future data sets, such as those from the Global Precipitation Measurement (GPM) mission. This presentation describes the data sets and services at the PDISC that are currently used by precipitation science and applications researchers, and which will be enhanced in preparation for GPM and associated multi-sensor data research. Specifically, the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) will be illustrated. Giovanni enables scientific exploration of Earth science data without researchers having to perform the complicated data access and match-up processes. In addition, PDISC tool and service capabilities being adapted for GPM data will be described, including the Google-like Mirador data search and access engine; semantic technology to help manage large amounts of multi-sensor data and their relationships; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion to various formats (e.g., netCDF, HDF, KML (for Google Earth)); visualization and analysis of Level 2 data profiles and maps; parameter and spatial subsetting; time and temporal aggregation; regridding; data version control and provenance; continuous archive verification; and expertise in data-related standards and interoperability. The goal of providing these services is to further the progress towards a common framework by which data analysis/validation can be more easily accomplished.

A detailed view of Earth across space and time: our changing planet through a 32-year global Landsat and Sentinel-2 timelapse video

NASA Astrophysics Data System (ADS)

Herwig, C.

2017-12-01

The Landsat program offers an unparalleled record of our changing planet, with satellites that have been observing the Earth since 1972 to the present day. However, clouds, seasonal variation, and technical challenges around access to large volumes of data make it difficult for researchers and the public to understand global and regional scale changes across time through the planetary dataset. Earth Timelapse is a global, zoomable video that has helped revolutionize how users - millions of which have never been capable of utilizing Landsat data before - monitor and understand a changing planet. It is made from 33 cloud-free annual mosaics, one for each year from 1984 to 2016, which are made interactively explorable by Carnegie Mellon University CREATE Lab's Time Machine library, a technology for creating and viewing zoomable and pannable timelapses over space and time. Using Earth Engine, we combined over 5 million satellite images acquired over the past three decades by 5 different satellites. The majority of the images come from Landsat, a joint USGS/NASA Earth observation program that has observed the Earth since the 1970s. For 2015 and 2016, we combined Landsat 8 imagery with imagery from Sentinel-2A, part of the European Commission and European Space Agency's Copernicus Earth observation program. Along with the interactive desktop Timelapse application, we created a 200-video YouTube playlist highlighting areas across the world exhibiting change in the dataset.Earth Timelapse is an example that illustrates the power of Google Earth Engine's cloud-computing platform, which enables users such as scientists, researchers, and journalists to detect changes, map trends, and quantify differences on the Earth's surface using Google's computational infrastructure and the multi-petabyte Earth Engine data catalog. Earth Timelapse also highlights the value of data visualization to communicate with non-scientific audiences with varied technical and internet connectivity. Timelapse videos - as a global, zoomable and explorable web map across time as well as curated locations hosted on YouTube - can be effective at conveying large and medium scale land surface changes over time to diverse audiences.

CEO Sites Mission Management System (SMMS)

NASA Technical Reports Server (NTRS)

Trenchard, Mike

2014-01-01

Late in fiscal year 2011, the Crew Earth Observations (CEO) team was tasked to upgrade its science site database management tool, which at the time was integrated with the Automated Mission Planning System (AMPS) originally developed for Earth Observations mission planning in the 1980s. Although AMPS had been adapted and was reliably used by CEO for International Space Station (ISS) payload operations support, the database structure was dated, and the compiler required for modifications would not be supported in the Windows 7 64-bit operating system scheduled for implementation the following year. The Sites Mission Management System (SMMS) is now the tool used by CEO to manage a heritage Structured Query Language (SQL) database of more than 2,000 records for Earth science sites. SMMS is a carefully designed and crafted in-house software package with complete and detailed help files available for the user and meticulous internal documentation for future modifications. It was delivered in February 2012 for test and evaluation. Following acceptance, it was implemented for CEO mission operations support in April 2012. The database spans the period from the earliest systematic requests for astronaut photography during the shuttle era to current ISS mission support of the CEO science payload. Besides logging basic image information (site names, locations, broad application categories, and mission requests), the upgraded database management tool now tracks dates of creation, modification, and activation; imagery acquired in response to requests; the status and location of ancillary site information; and affiliations with studies, their sponsors, and collaborators. SMMS was designed to facilitate overall mission planning in terms of site selection and activation and provide the necessary site parameters for the Satellite Tool Kit (STK) Integrated Message Production List Editor (SIMPLE), which is used by CEO operations to perform daily ISS mission planning. The CEO team uses the SMMS for three general functions - database queries of content and status, individual site creation and updates, and mission planning. The CEO administrator of the science site database is able to create or modify the content of sites and activate or deactivate them based on the requirements of the sponsors. The administrator supports and implements ISS mission planning by assembling, reporting, and activating mission-specific site selections for management; deactivating sites as requirements are met; and creating new sites, such as International Charter sites for disasters, as circumstances warrant. In addition to the above CEO internal uses, when site planning for a specific ISS mission is complete and approved, the SMMS can produce and export those essential site database elements for the mission into XML format for use by onboard Earth-location systems, such as Worldmap. The design, development, and implementation of the SMMS resulted in a superior database management system for CEO science sites by focusing on the functions and applications of the database alone instead of integrating the database with the multipurpose configuration of the AMPS. Unlike the AMPS, it can function and be modified within the existing Windows 7 environment. The functions and applications of the SMMS were expanded to accommodate more database elements, report products, and a streamlined interface for data entry and review. A particularly elegant enhancement in data entry was the integration of the Google Earth application for the visual display and definition of site coordinates for site areas defined by multiple coordinates. Transfer between the SMMS and Google Earth is accomplished with a Keyhole Markup Language (KML) expression of geographic data (see figures 3 and 4). Site coordinates may be entered into the SMMS panel directly for display in Google Earth, or the coordinates may be defined on the Google Earth display as a mouse-controlled polygonal definition and transferred back into the SMMS as KML input. This significantly reduces the possibility of errors in coordinate entries and provides visualization of the scale of the site being defined. CEO now has a powerful tool for managing and defining sites on the Earth's surface for both targets of astronaut photography or other onboard remote sensing systems. It can also record and track results by sponsor, collaborator, or type of study.

75 FR 359 - Google Energy LLC; Supplemental Notice That Initial Market-Based Rate Filing Includes Request for...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-05

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. ER10-468-000] Google Energy LLC; Supplemental Notice That Initial Market-Based Rate Filing Includes Request for Blanket Section... of Google Energy LLC's application for market-based rate authority, with an accompanying rate tariff...

Network-Based Mitigation of Illegal Immigration in Aegean Sea (Greece)

DTIC Science & Technology

2010-09-01

From Google- Images ) ...........................................1 Figure 2. The perilous trip (From Google- Images ...2 Figure 3. EU countries (From Google- Images ).................................................................3 Figure 4...Eastern Aegen Sea and territorial water line (From Google- Images )................4 Figure 5. Cross-border zone

Cloud Geospatial Analysis Tools for Global-Scale Comparisons of Population Models for Decision Making

NASA Astrophysics Data System (ADS)

Hancher, M.; Lieber, A.; Scott, L.

2017-12-01

The volume of satellite and other Earth data is growing rapidly. Combined with information about where people are, these data can inform decisions in a range of areas including food and water security, disease and disaster risk management, biodiversity, and climate adaptation. Google's platform for planetary-scale geospatial data analysis, Earth Engine, grants access to petabytes of continually updating Earth data, programming interfaces for analyzing the data without the need to download and manage it, and mechanisms for sharing the analyses and publishing results for data-driven decision making. In addition to data about the planet, data about the human planet - population, settlement and urban models - are now available for global scale analysis. The Earth Engine APIs enable these data to be joined, combined or visualized with economic or environmental indicators such as nighttime lights trends, global surface water, or climate projections, in the browser without the need to download anything. We will present our newly developed application intended to serve as a resource for government agencies, disaster response and public health programs, or other consumers of these data to quickly visualize the different population models, and compare them to ground truth tabular data to determine which model suits their immediate needs. Users can further tap into the power of Earth Engine and other Google technologies to perform a range of analysis from simple statistics in custom regions to more complex machine learning models. We will highlight case studies in which organizations around the world have used Earth Engine to combine population data with multiple other sources of data, such as water resources and roads data, over deep stacks of temporal imagery to model disease risk and accessibility to inform decisions.

Get Connected

ERIC Educational Resources Information Center

Horton, Jessica; Hagevik, Rita; Adkinson, Bennett; Parmly, Jilynn

2013-01-01

Technology can be both a blessing and a curse in the classroom. Although technology can provide greater access to information and increase student engagement, if screen time replaces time spent outside, then students stand to lose awareness and connectivity to the surrounding natural environment. This article describes how Google Earth can foster…

Cornerstone: Foundational Models and Services for Integrated Battle Planning

DTIC Science & Technology

2012-06-01

We close with a summary of future planned research. 3 Cross-Domain Knowledge Representation One of the primary reasons behind the...mission data using Google Earth to display the results of a Keyhole Markup Language (KML) mission data translator. Finally, we successfully ran Thread 1

Taking advantage of Google's Web-based applications and services.

PubMed

Brigham, Tara J

2014-01-01

Google is a company that is constantly expanding and growing its services and products. While most librarians possess a "love/hate" relationship with Google, there are a number of reasons you should consider exploring some of the tools Google has created and made freely available. Applications and services such as Google Docs, Slides, and Google+ are functional and dynamic without the cost of comparable products. This column will address some of the issues users should be aware of before signing up to use Google's tools, and a description of some of Google's Web applications and services, plus how they can be useful to librarians in health care.

A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing

USGS Publications Warehouse

Biradar, C.M.; Thenkabail, P.S.; Noojipady, P.; Li, Y.; Dheeravath, V.; Turral, H.; Velpuri, M.; Gumma, M.K.; Gangalakunta, O.R.P.; Cai, X.L.; Xiao, X.; Schull, M.A.; Alankara, R.D.; Gunasinghe, S.; Mohideen, S.

2009-01-01

The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2-8%, and the errors of commission of 19-36%. ?? 2008 Elsevier B.V.

An Inverse MOOC Model: Small Virtual Field Geology Classes with Many Teachers (Invited)

NASA Astrophysics Data System (ADS)

De Paor, D. G.; Whitmeyer, S. J.; Bentley, C.

2013-12-01

In the Massive Open Online Courses (MOOCs) mode of instruction, one or a small group of collaborating instructors lecture online to a large (often extremely large) number of students. We are experimenting with an inverse concept: an online classroom in which a small group of collaborating students are taught by dozens of collaborating instructors. This experiment is part of a new NSF TUES Type 3 project titled 'Google Earth for Onsite and Distance Education (GEODE).' Among the goals of the project are the development of an online course called the 'Grand Tour.' We are inviting dozens of colleagues to record virtual field trips (VFTs) and upload them to Google Earth. Students enrolled in the course will be assigned to a small group and tasked with a research project--for example to write a report on foreland thrust belts. They will select a small subset of available VFTs to follow and will be scaffolded by virtual specimens, emergent cross sections, analytical simulations (virtual tricorders), and a game style environment. Instant feedback based on auto-logging will enable adaptive learning. The design is suited to both onsite and distance education and will facilitate access to iconic geologic sites around the world to persons with mobility constraints. We invite input from the community to help guide the design phase of this project. Prototypes of the above-listed learning resources have already been developed and are freely available at http://www.DigitalPlanet.org.

Internet-Based Software Tools for Analysis and Processing of LIDAR Point Cloud Data via the OpenTopography Portal

NASA Astrophysics Data System (ADS)

Nandigam, V.; Crosby, C. J.; Baru, C.; Arrowsmith, R.

2009-12-01

LIDAR is an excellent example of the new generation of powerful remote sensing data now available to Earth science researchers. Capable of producing digital elevation models (DEMs) more than an order of magnitude higher resolution than those currently available, LIDAR data allows earth scientists to study the processes that contribute to landscape evolution at resolutions not previously possible, yet essential for their appropriate representation. Along with these high-resolution datasets comes an increase in the volume and complexity of data that the user must efficiently manage and process in order for it to be scientifically useful. Although there are expensive commercial LIDAR software applications available, processing and analysis of these datasets are typically computationally inefficient on the conventional hardware and software that is currently available to most of the Earth science community. We have designed and implemented an Internet-based system, the OpenTopography Portal, that provides integrated access to high-resolution LIDAR data as well as web-based tools for processing of these datasets. By using remote data storage and high performance compute resources, the OpenTopography Portal attempts to simplify data access and standard LIDAR processing tasks for the Earth Science community. The OpenTopography Portal allows users to access massive amounts of raw point cloud LIDAR data as well as a suite of DEM generation tools to enable users to generate custom digital elevation models to best fit their science applications. The Cyberinfrastructure software tools for processing the data are freely available via the portal and conveniently integrated with the data selection in a single user-friendly interface. The ability to run these tools on powerful Cyberinfrastructure resources instead of their own labs provides a huge advantage in terms of performance and compute power. The system also encourages users to explore data processing methods and the variations in algorithm parameters since all of the processing is done remotely and numerous jobs can be submitted in sequence. The web-based software also eliminates the need for users to deal with the hassles and costs associated with software installation and licensing while providing adequate disk space for storage and personal job archival capability. Although currently limited to data access and DEM generation tasks, the OpenTopography system is modular in design and can be modified to accommodate new processing tools as they become available. We are currently exploring implementation of higher-level DEM analysis tasks in OpenTopography, since such processing is often computationally intensive and thus lends itself to utilization of cyberinfrastructure. Products derived from OpenTopography processing are available in a variety of formats ranging from simple Google Earth visualizations of LIDAR-derived hillshades to various GIS-compatible grid formats. To serve community users less interested in data processing, OpenTopography also hosts 1 km^2 digital elevation model tiles as well as Google Earth image overlays for a synoptic view of the data.

Experential Learning Approach For Training Pre-Service Teachers In Environmental Science Using Mobile Apps

NASA Astrophysics Data System (ADS)

Senan, D. C.; Nair, U. S.

2015-12-01

In the context of complex environmental problems facing societies, environmental education is becoming an integral part of curriculum all levels of education, including teacher education. Traditional teaching methodology is often criticized for being reductionist and empirical and thus not optimal for training next generation of students who are expected to formulate solutions to complex, interdisciplinary environmental issues. This study will report on the use of mobile application, based on the Open Data Kit (ODK), along with the Google Earth Engine (GEE) to implement a better approach, namely experiential learning, for teacher education in Kerala, India. The specific topic considered is land use and land cover change due to human activity. The experiential learning approach implemented will involve students using Android mobile application to collect a sample of geo-locations for different land cover types. This data will be used to classify satellite imagery within Google Earth Engine and used to understand how their neighborhoods have changed over the years. Rather than being passive information recipients, the students will develop understanding based on their own analysis of how urban regions grow, crop lands shrink and forests disappear. This study will report on the implementation of experiential learning approach through the use of ODK and GEE, and on the ongoing evaluation of effectiveness of experiential learning approach for environmental education. A Pretest-Posttest study design will be used for evaluation. Change in environmental consciousness, as characterized by a well-designed and validated Environmental Consciousness Scale will be determined for a study group of 300 Pre-service teachers of Kerala, India. The significance between the mean scores of the data collected during pretest and posttest will be analyzed using paired t tests. Qualitative feedback about the Mobile Application through focus group interviews will also collected and analyzed.

Integrating Geospatial Technologies to Examine Urban Land Use Change: A Design Partnership

ERIC Educational Resources Information Center

Bodzin, Alec M.; Cirucci, Lori

2009-01-01

This article describes a design partnership that investigated how to integrate Google Earth, remotely sensed satellite and aerial imagery, with other instructional resources to investigate ground cover and land use in diverse middle school classrooms. Data analysis from the implementation study revealed that students acquired skills for…

Learning Geomorphology Using Aerial Photography in a Web-Facilitated Class

ERIC Educational Resources Information Center

Palmer, R. Evan

2013-01-01

General education students taking freshman-level physical geography and geomorphology classes at Arizona State University completed an online laboratory whose main tool was Google Earth. Early in the semester, oblique and planimetric views introduced students to a few volcanic, tectonic, glacial, karst, and coastal landforms. Semi-quantitative…

NASA Earth Observations (NEO): Data Imagery for Education and Visualization

NASA Astrophysics Data System (ADS)

Ward, K.

2008-12-01

NASA Earth Observations (NEO) has dramatically simplified public access to georeferenced imagery of NASA remote sensing data. NEO targets the non-traditional data users who are currently underserved by functionality and formats available from the existing data ordering systems. These users include formal and informal educators, museum and science center personnel, professional communicators, and citizen scientists. NEO currently serves imagery from 45 different datasets with daily, weekly, and/or monthly temporal resolutions, with more datasets currently under development. The imagery from these datasets is produced in coordination with several data partners who are affiliated either with the instrument science teams or with the respective data processing center. NEO is a system of three components -- website, WMS (Web Mapping Service), and ftp archive -- which together are able to meet the wide-ranging needs of our users. Some of these needs include the ability to: view and manipulate imagery using the NEO website -- e.g., applying color palettes, resizing, exporting to a variety of formats including PNG, JPEG, KMZ (Google Earth), GeoTIFF; access the NEO collection via a standards-based API (WMS); and create customized exports for select users (ftp archive) such as Science on a Sphere, NASA's Earth Observatory, and others.

Investigating Climate Change Issues With Web-Based Geospatial Inquiry Activities

NASA Astrophysics Data System (ADS)

Dempsey, C.; Bodzin, A. M.; Sahagian, D. L.; Anastasio, D. J.; Peffer, T.; Cirucci, L.

2011-12-01

In the Environmental Literacy and Inquiry middle school Climate Change curriculum we focus on essential climate literacy principles with an emphasis on weather and climate, Earth system energy balance, greenhouse gases, paleoclimatology, and how human activities influence climate change (http://www.ei.lehigh.edu/eli/cc/). It incorporates a related set of a framework and design principles to provide guidance for the development of the geospatial technology-integrated Earth and environmental science curriculum materials. Students use virtual globes, Web-based tools including an interactive carbon calculator and geologic timeline, and inquiry-based lab activities to investigate climate change topics. The curriculum includes educative curriculum materials that are designed to promote and support teachers' learning of important climate change content and issues, geospatial pedagogical content knowledge, and geographic spatial thinking. The curriculum includes baseline instructional guidance for teachers and provides implementation and adaptation guidance for teaching with diverse learners including low-level readers, English language learners and students with disabilities. In the curriculum, students use geospatial technology tools including Google Earth with embedded spatial data to investigate global temperature changes, areas affected by climate change, evidence of climate change, and the effects of sea level rise on the existing landscape. We conducted a designed-based research implementation study with urban middle school students. Findings showed that the use of the Climate Change curriculum showed significant improvement in urban middle school students' understanding of climate change concepts.

A Solid Earth educational module, co-operatively developed by scientists and high school teachers through the Scripps Classroom Connection GK12 Program

NASA Astrophysics Data System (ADS)

Ziegler, L. B.; van Dusen, D.; Benedict, R.; Chojnacki, P. R.; Peach, C. L.; Staudigel, H.; Constable, C.; Laske, G.

2010-12-01

The Scripps Classroom Connection, funded through the NSF GK-12 program, pairs local high school teachers with Scripps Institution of Oceanography (SIO) graduate students in the earth and ocean sciences for their mutual professional development. An integral goal of the program is the collaborative production of quality earth science educational modules that are tested in the classroom and subsequently made freely available online for use by other educators. We present a brief overview of the program structure in place to support this goal and illustrate a module that we have developed on the Solid Earth & Plate Tectonics for a 9th grade Earth Science classroom. The unit includes 1) an exercise in constructing a geomagnetic polarity timescale which exposes students to authentic scientific data; 2) activities, labs, lectures and worksheets that support the scientific content; and 3) use of online resources such as Google Earth and interactive animations that help students better understand the concepts. The educational unit is being implemented in two separate local area high schools for Fall 2010 and we will report on our experiences. The co-operative efforts of teachers and scientists lead to educational materials which expose students to the scientific process and current science research, while teaching basic concepts using an engaging inquiry-based approach. In turn, graduate students involved gain experience communicating their science to non-science audiences.

UNAVCO Software and Services for Visualization and Exploration of Geoscience Data

NASA Astrophysics Data System (ADS)

Meertens, C.; Wier, S.

2007-12-01

UNAVCO has been involved in visualization of geoscience data to support education and research for several years. An early and ongoing service is the Jules Verne Voyager, a web browser applet built on the GMT that displays any area on Earth, with many data set choices, including maps, satellite images, topography, geoid heights, sea-floor ages, strain rates, political boundaries, rivers and lakes, earthquake and volcano locations, focal mechanisms, stress axes, and observed and modeled plate motion and deformation velocity vectors from geodetic measurements around the world. As part of the GEON project, UNAVCO has developed the GEON IDV, a research-level, 4D (earth location, depth and/or altitude, and time), Java application for interactive display and analysis of geoscience data. The GEON IDV is designed to meet the challenge of investigating complex, multi-variate, time-varying, three-dimensional geoscience data anywhere on earth. The GEON IDV supports simultaneous displays of data sets from differing sources, with complete control over colors, time animation, map projection, map area, point of view, and vertical scale. The GEON IDV displays gridded and point data, images, GIS shape files, and several other types of data. The GEON IDV has symbols and displays for GPS velocity vectors, seismic tomography, earthquake focal mechanisms, earthquake locations with magnitude or depth, seismic ray paths in 3D, seismic anisotropy, convection model visualization, earth strain axes and strain field imagery, and high-resolution 3D topographic relief maps. Multiple data sources and display types may appear in one view. As an example of GEON IDV utility, it can display hypocenters under a volcano, a surface geology map of the volcano draped over 3D topographic relief, town locations and political boundaries, and real-time 3D weather radar clouds of volcanic ash in the atmosphere, with time animation. The GEON IDV can drive a GeoWall or other 3D stereo system. IDV output includes imagery, movies, and KML files for Google Earth use of IDV static images, where Google Earth can handle the display. The IDV can be scripted to create display images on user request or automatically on data arrival, offering the use of the IDV as a back end to support a data web site. We plan to extend the power of the IDV by accepting new data types and data services, such as GeoSciML. An active program of online and video training in GEON IDV use is planned. UNAVCO will support users who need assistance converting their data to the standard formats used by the GEON IDV. The UNAVCO Facility provides web-accessible support for Google Earth and Google Maps display of any of more than 9500 GPS stations and survey points, including metadata for each installation. UNAVCO provides corresponding Open Geospatial Consortium (OGC) web services with the same data. UNAVCO's goal is to facilitate data access, interoperability, and efficient searches, exploration, and use of data by promoting web services, standards for GEON IDV data formats and metadata, and software able to simultaneously read and display multiple data sources, formats, and map locations or projections. Retention and propagation of semantics and metadata with observational and experimental values is essential for interoperability and understanding diverse data sources.

Geospatial Visualization of Scientific Data Through Keyhole Markup Language

NASA Astrophysics Data System (ADS)

Wernecke, J.; Bailey, J. E.

2008-12-01

The development of virtual globes has provided a fun and innovative tool for exploring the surface of the Earth. However, it has been the paralleling maturation of Keyhole Markup Language (KML) that has created a new medium and perspective through which to visualize scientific datasets. Originally created by Keyhole Inc., and then acquired by Google in 2004, in 2007 KML was given over to the Open Geospatial Consortium (OGC). It became an OGC international standard on 14 April 2008, and has subsequently been adopted by all major geobrowser developers (e.g., Google, Microsoft, ESRI, NASA) and many smaller ones (e.g., Earthbrowser). By making KML a standard at a relatively young stage in its evolution, developers of the language are seeking to avoid the issues that plagued the early World Wide Web and development of Hypertext Markup Language (HTML). The popularity and utility of Google Earth, in particular, has been enhanced by KML features such as the Smithsonian volcano layer and the dynamic weather layers. Through KML, users can view real-time earthquake locations (USGS), view animations of polar sea-ice coverage (NSIDC), or read about the daily activities of chimpanzees (Jane Goodall Institute). Perhaps even more powerful is the fact that any users can create, edit, and share their own KML, with no or relatively little knowledge of manipulating computer code. We present an overview of the best current scientific uses of KML and a guide to how scientists can learn to use KML themselves.

Application of Deep Learning in GLOBELAND30-2010 Product Refinement

NASA Astrophysics Data System (ADS)

Liu, T.; Chen, X.

2018-04-01

GlobeLand30, as one of the best Global Land Cover (GLC) product at 30-m resolution, has been widely used in many research fields. Due to the significant spectral confusion among different land cover types and limited textual information of Landsat data, the overall accuracy of GlobeLand30 is about 80 %. Although such accuracy is much higher than most other global land cover products, it cannot satisfy various applications. There is still a great need of an effective method to improve the quality of GlobeLand30. The explosive high-resolution satellite images and remarkable performance of Deep Learning on image classification provide a new opportunity to refine GlobeLand30. However, the performance of deep leaning depends on quality and quantity of training samples as well as model training strategy. Therefore, this paper 1) proposed an automatic training sample generation method via Google earth to build a large training sample set; and 2) explore the best training strategy for land cover classification using GoogleNet (Inception V3), one of the most widely used deep learning network. The result shows that the fine-tuning from first layer of Inception V3 using rough large sample set is the best strategy. The retrained network was then applied in one selected area from Xi'an city as a case study of GlobeLand30 refinement. The experiment results indicate that the proposed approach with Deep Learning and google earth imagery is a promising solution for further improving accuracy of GlobeLand30.

Application based on ArcObject inquiry and Google maps demonstration to real estate database

NASA Astrophysics Data System (ADS)

Hwang, JinTsong

2007-06-01

Real estate industry in Taiwan has been flourishing in recent years. To acquire various and abundant information of real estate for sale is the same goal for the consumers and the brokerages. Therefore, before looking at the property, it is important to get all pertinent information possible. Not only this beneficial for the real estate agent as they can provide the sellers with the most information, thereby solidifying the interest of the buyer, but may also save time and the cost of manpower were something out of place. Most of the brokerage sites are aware of utilizes Internet as form of media for publicity however; the contents are limited to specific property itself and the functions of query are mostly just provided searching by condition. This paper proposes a query interface on website which gives function of zone query by spatial analysis for non-GIS users, developing a user-friendly interface with ArcObject in VB6, and query by condition. The inquiry results can show on the web page which is embedded functions of Google Maps and the UrMap API on it. In addition, the demonstration of inquiry results will give the multimedia present way which includes hyperlink to Google Earth with surrounding of the property, the Virtual Reality scene of house, panorama of interior of building and so on. Therefore, the website provides extra spatial solution for query and demonstration abundant information of real estate in two-dimensional and three-dimensional types of view.

The Dimensions of the Solar System

ERIC Educational Resources Information Center

Schneider, Stephen E.; Davis, Kathleen S.

2007-01-01

A few new wrinkles have been added to the popular activity of building a scale model of the solar system. Students can learn about maps and scaling using easily accessible online resources that include satellite images. This is accomplished by taking advantage of some of the special features of Google Earth. This activity gives students a much…

Crowdfunding Astronomy Research with Google Sky

ERIC Educational Resources Information Center

Metcalfe, Travis S.

2015-01-01

For nearly four years, NASA's Kepler space telescope searched for planets like Earth around more than 150,000 stars similar to the Sun. In 2008 with in-kind support from several technology companies, our non-profit organization established the Pale Blue Dot Project, an adopt-a-star program that supports scientific research on the stars observed by…

Using Google Earth to Teach Plate Tectonics and Science Explanations

ERIC Educational Resources Information Center

Blank, Lisa M.; Plautz, Mike; Almquist, Heather; Crews, Jeff; Estrada, Jen

2012-01-01

"A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas" emphasizes that the practice of science is inherently a model-building activity focused on constructing explanations using evidence and reasoning (NRC 2012). Because building and refining is an iterative process, middle school students may view this practice…

Drawing the Line with Google Earth: The Place of Digital Mapping outside of Geography

ERIC Educational Resources Information Center

Mercier, O. Ripeka; Rata, Arama

2017-01-01

The "Te Kawa a Maui Atlas" project explores how mapping activities support undergraduate student engagement and learning in Maori studies. This article describes two specific assignments, which used online mapping allowing students to engage with the work of their peers. By analysing student evaluations of these activities, we identify…

Map Scale, Proportion, and Google[TM] Earth

ERIC Educational Resources Information Center

Roberge, Martin C.; Cooper, Linda L.

2010-01-01

Aerial imagery has a great capacity to engage and maintain student interest while providing a contextual setting to strengthen their ability to reason proportionally. Free, on-demand, high-resolution, large-scale aerial photography provides both a bird's eye view of the world and a new perspective on one's own community. This article presents an…

Detecting Potential Water Quality Issues by Mapping Trophic Status Using Google Earth Engine

NASA Astrophysics Data System (ADS)

Nguy-Robertson, A. L.; Harvey, K.; Huening, V.; Robinson, H.

2017-12-01

The identification, timing, and spatial distribution of recurrent algal blooms and aquatic vegetation can help water managers and policy makers make better water resource decisions. In many parts of the world there is little monitoring or reporting of water quality due to the required costs and effort to collect and process water samples. We propose to use Google Earth Engine to quickly identify the recurrence of trophic states in global inland water systems. Utilizing Landsat and Sentinel multispectral imagery, inland water quality parameters (i.e. chlorophyll a concentration) can be estimated and waters can be classified by trophic state; oligotrophic, mesotrophic, eutrophic, and hypereutrophic. The recurrence of eutrophic and hypereutrophic observations can highlight potentially problematic locations where algal blooms or aquatic vegetation occur routinely. Eutrophic and hypereutrophic waters commonly include many harmful algal blooms and waters prone to fish die-offs from hypoxia. While these maps may be limited by the accuracy of the algorithms utilized to estimate chlorophyll a; relative comparisons at a local scale can help water managers to focus limited resources.

Undergraduate Course on Global Concerns

NASA Astrophysics Data System (ADS)

Richard, G. A.; Weidner, D. J.

2008-12-01

GEO 311: Geoscience and Global Concerns is an undergraduate course taught at Stony Brook University during each fall semester. The class meets twice per week, with one session consisting of a lecture and the other, an interactive activity in a computer laboratory that engages the students in exploring real world problems. A specific concern or issue serves as a focus during each session. The students are asked to develop answers to a series of questions that engage them in identifying causes of the problem, connections with the Earth system, relationships to other problems, and possible solutions on both a global and local scale. The questions are designed to facilitate an integrated view of the Earth system. Examples of topics that the students explore during the laboratory sessions are: 1) fossil fuel reserves and consumption rates and the effect of their use on climate, 2) alternative sources of energy and associated technologies, such as solar photovoltaics, nuclear energy, tidal power, geothermal energy, and wind power, 3) effects of tsunamis and earthquakes on human populations and infrastructure, 4) climate change, and 5) hurricanes and storms. The selection and scheduling of topics often takes advantage of the occurrence of media attention or events that can serve as case studies. Tools used during the computer sessions include Google Earth, ArcGIS, spreadsheets, and web sites that offer data and maps. The students use Google Earth or ArcGIS to map events such as earthquakes, storms, tsunamis, and changes in the extent of polar ice. Spreadsheets are employed to discern trends in fossil fuel supply and consumption, and to experiment with models that make predictions for the future. We present examples of several of these activities and discuss how they facilitate an understanding of interrelationships within the Earth system.

A Highly Scalable Data Service (HSDS) using Cloud-based Storage Technologies for Earth Science Data

NASA Astrophysics Data System (ADS)

Michaelis, A.; Readey, J.; Votava, P.; Henderson, J.; Willmore, F.

2017-12-01

Cloud based infrastructure may offer several key benefits of scalability, built in redundancy, security mechanisms and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and legacy software systems developed for online data repositories within the federal government were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Moreover, services bases on object storage are well established and provided through all the leading cloud service providers (Amazon Web Service, Microsoft Azure, Google Cloud, etc…) of which can often provide unmatched "scale-out" capabilities and data availability to a large and growing consumer base at a price point unachievable from in-house solutions. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows a performance advantage for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.

A virtual tour of geological heritage: Valourising geodiversity using Google Earth and QR code

NASA Astrophysics Data System (ADS)

Martínez-Graña, A. M.; Goy, J. L.; Cimarra, C. A.

2013-12-01

When making land-use plans, it is necessary to inventory and catalogue the geological heritage and geodiversity of a site to establish an apolitical conservation protection plan to meet the educational and social needs of society. New technologies make it possible to create virtual databases using virtual globes - e.g., Google Earth - and other personal-use geomatics applications (smartphones, tablets, PDAs) for accessing geological heritage information in “real time” for scientific, educational, and cultural purposes via a virtual geological itinerary. Seventeen mapped and georeferenced geosites have been created in Keyhole Markup Language for use in map layers used in geological itinerary stops for different applications. A virtual tour has been developed for Las Quilamas Natural Park, which is located in the Spanish Central System, using geological layers and topographic and digital terrain models that can be overlaid in a 3D model. The Google Earth application was used to import the geosite placemarks. For each geosite, a tab has been developed that shows a description of the geology with photographs and diagrams and that evaluates the scientific, educational, and tourism quality. Augmented reality allows the user to access these georeferenced thematic layers and overlay data, images, and graphics in real time on their mobile devices. These virtual tours can be incorporated into subject guides designed by public. Seven educational and interpretive panels describing some of the geosites were designed and tagged with a QR code that could be printed at each stop or in the printed itinerary. These QR codes can be scanned with the camera found on most mobile devices, and video virtual tours can be viewed on these devices. The virtual tour of the geological heritage can be used to show tourists the geological history of the Las Quilamas Natural Park using new geomatics technologies (virtual globes, augmented reality, and QR codes).

Next Generation Landsat Products Delivered Using Virtual Globes and OGC Standard Services

NASA Astrophysics Data System (ADS)

Neiers, M.; Dwyer, J.; Neiers, S.

2008-12-01

The Landsat Data Continuity Mission (LDCM) is the next in the series of Landsat satellite missions and is tasked with the objective of delivering data acquired by the Operational Land Imager (OLI). The OLI instrument will provide data continuity to over 30 years of global multispectral data collected by the Landsat series of satellites. The U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center has responsibility for the development and operation of the LDCM ground system. One of the mission objectives of the LDCM is to distribute OLI data products electronically over the Internet to the general public on a nondiscriminatory basis and at no cost. To ensure the user community and general public can easily access LDCM data from multiple clients, the User Portal Element (UPE) of the LDCM ground system will use OGC standards and services such as Keyhole Markup Language (KML), Web Map Service (WMS), Web Coverage Service (WCS), and Geographic encoding of Really Simple Syndication (GeoRSS) feeds for both access to and delivery of LDCM products. The USGS has developed and tested the capabilities of several successful UPE prototypes for delivery of Landsat metadata, full resolution browse, and orthorectified (L1T) products from clients such as Google Earth, Google Maps, ESRI ArcGIS Explorer, and Microsoft's Virtual Earth. Prototyping efforts included the following services: using virtual globes to search the historical Landsat archive by dynamic generation of KML; notification of and access to new Landsat acquisitions and L1T downloads from GeoRSS feeds; Google indexing of KML files containing links to full resolution browse and data downloads; WMS delivery of reduced resolution browse, full resolution browse, and cloud mask overlays; and custom data downloads using WCS clients. These various prototypes will be demonstrated and LDCM service implementation plans will be discussed during this session.

Preparing Precipitation Data Access, Value-added Services and Scientific Exploration Tools for the Integrated Multi-satellitE Retrievals for GPM (IMERG)

NASA Astrophysics Data System (ADS)

Ostrenga, D.; Liu, Z.; Kempler, S. J.; Vollmer, B.; Teng, W. L.

2013-12-01

The Precipitation Data and Information Services Center (PDISC) (http://disc.gsfc.nasa.gov/precipitation or google: NASA PDISC), located at the NASA Goddard Space Flight Center (GSFC) Earth Sciences (GES) Data and Information Services Center (DISC), is home of the Tropical Rainfall Measuring Mission (TRMM) data archive. For over 15 years, the GES DISC has served not only TRMM, but also other space-based, airborne-based, field campaign and ground-based precipitation data products to the precipitation community and other disciplinary communities as well. The TRMM Multi-Satellite Precipitation Analysis (TMPA) products are the most popular products in the TRMM product family in terms of data download and access through Mirador, the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) and other services. The next generation of TMPA, the Integrated Multi-satellitE Retrievals for GPM (IMERG) to be released in 2014 after the launch of GPM, will be significantly improved in terms of spatial and temporal resolutions. To better serve the user community, we are preparing data services and samples are listed below. To enable scientific exploration of Earth science data products without going through complicated and often time consuming processes, such as data downloading, data processing, etc., the GES DISC has developed Giovanni in consultation with members of the user community, requesting quick search, subset, analysis and display capabilities for their specific data of interest. For example, the TRMM Online Visualization and Analysis System (TOVAS, http://disc2.nascom.nasa.gov/Giovanni/tovas/) has proven extremely popular, especially as additional datasets have been added upon request. Giovanni will continue to evolve to accommodate GPM data and the multi-sensor data inter-comparisons that will be sure to follow. Additional PDISC tool and service capabilities being adapted for GPM data include: An on-line PDISC Portal (includes user guide, etc.); Data ingest, processing, distribution from on-line archive; Google-like Mirador data search and access engine; electronic distribution, Subscriptions; Uses semantic technology to help manage large amounts of multi-sensor data and their relationships; Data drill down and search capabilities; Data access through various web services, i.e., OPeNDAP, GDS, WMS, WCS; Conversion into various formats, e.g., netCDF, HDF, KML (for Google Earth), ascii; Exploration, visualization and statistical online analysis through Giovanni; Visualization and analysis of L2 data profiles and maps; Generation of derived products, such as, daily products; Parameter and spatial subsetting; Time and temporal aggregation; Regridding; Data version control and provenance; Data Stewardship - Continuous archive verification; Documentation; Science support for proper data usage, help desk; Monitoring services for applications; Expertise in data related standards and interoperability. This presentation will further describe the data services at the PDISC that are currently being utilized by precipitation science and application researchers, and the preparation plan for IMERG. Comments and feedback are welcome.

Assessing the Usefulness of Google Books’ Word Frequencies for Psycholinguistic Research on Word Processing

PubMed Central

Brysbaert, Marc; Keuleers, Emmanuel; New, Boris

2011-01-01

In this Perspective Article we assess the usefulness of Google's new word frequencies for word recognition research (lexical decision and word naming). We find that, despite the massive corpus on which the Google estimates are based (131 billion words from books published in the United States alone), the Google American English frequencies explain 11% less of the variance in the lexical decision times from the English Lexicon Project (Balota et al., 2007) than the SUBTLEX-US word frequencies, based on a corpus of 51 million words from film and television subtitles. Further analyses indicate that word frequencies derived from recent books (published after 2000) are better predictors of word processing times than frequencies based on the full corpus, and that word frequencies based on fiction books predict word processing times better than word frequencies based on the full corpus. The most predictive word frequencies from Google still do not explain more of the variance in word recognition times of undergraduate students and old adults than the subtitle-based word frequencies. PMID:21713191

Learning to Be a Programmer in a Complex Organization: A Case Study on Practice-Based Learning during the Onboarding Process at Google

ERIC Educational Resources Information Center

Johnson, Maggie; Senges, Max

2010-01-01

Purpose: This paper seeks to analyse the effectiveness and impact of how Google currently trains its new software engineers ("Nooglers") to become productive in the software engineering community. The research focuses on the institutions and support for practice-based learning and cognitive apprenticeship in the Google environment.…

MARs Tools for Interactive ANalysis (MARTIAN): Google Maps Tools for Visual Exploration of Geophysical Modeling on Mars

NASA Astrophysics Data System (ADS)

Dimitrova, L. L.; Haines, M.; Holt, W. E.; Schultz, R. A.; Richard, G.; Haines, A. J.

2006-12-01

Interactive maps of surface-breaking faults and stress models on Mars provide important tools to engage undergraduate students, educators, and scientists with current geological and geophysical research. We have developed a map based on the Google Maps API -- an Internet based tool combining DHTML and AJAX, -- which allows very large maps to be viewed over the World Wide Web. Typically, small portions of the maps are downloaded as needed, rather than the entire image at once. This set-up enables relatively fast access for users with low bandwidth. Furthermore, Google Maps provides an extensible interactive interface making it ideal for visualizing multiple data sets at the user's choice. The Google Maps API works primarily with data referenced to latitudes and longitudes, which is then mapped in Mercator projection only. We have developed utilities for general cylindrical coordinate systems by converting these coordinates into equivalent Mercator projection before including them on the map. The MARTIAN project is available at http://rock.geo.sunysb.edu/~holt/Mars/MARTIAN/. We begin with an introduction to the Martian surface using a topography model. Faults from several datasets are classified by type (extension vs. compression) and by time epoch. Deviatoric stresses due to gravitational potential energy differences, calculated from the topography and crustal thickness, can be overlain. Several quantitative measures for the fit of the stress field to the faults are also included. We provide introductory text and exercises spanning a range of topics: how are faults identified, what stress is and how it relates to faults, what gravitational potential energy is and how variations in it produce stress, how the models are created, and how these models can be evaluated and interpreted. The MARTIAN tool is used at Stony Brook University in GEO 310: Introduction to Geophysics, a class geared towards junior and senior geosciences majors. Although this project is in its early stages, high school and college teachers, as well as researchers have expressed interest in using and extending these tools for visualizing and interacting with data on Earth and other planetary bodies.

Soil erodibility mapping using the RUSLE model to prioritize erosion control in the Wadi Sahouat basin, North-West of Algeria.

PubMed

Toubal, Abderrezak Kamel; Achite, Mohammed; Ouillon, Sylvain; Dehni, Abdelatif

2018-03-12

Soil losses must be quantified over watersheds in order to set up protection measures against erosion. The main objective of this paper is to quantify and to map soil losses in the Wadi Sahouat basin (2140 km 2 ) in the north-west of Algeria, using the Revised Universal Soil Loss Equation (RUSLE) model assisted by a Geographic Information System (GIS) and remote sensing. The Model Builder of the GIS allowed the automation of the different operations for establishing thematic layers of the model parameters: the erosivity factor (R), the erodibility factor (K), the topographic factor (LS), the crop management factor (C), and the conservation support practice factor (P). The average annual soil loss rate in the Wadi Sahouat basin ranges from 0 to 255 t ha -1  year -1 , maximum values being observed over steep slopes of more than 25% and between 600 and 1000 m elevations. 3.4% of the basin is classified as highly susceptible to erosion, 4.9% with a medium risk, and 91.6% at a low risk. Google Earth reveals a clear conformity with the degree of zones to erosion sensitivity. Based on the soil loss map, 32 sub-basins were classified into three categories by priority of intervention: high, moderate, and low. This priority is available to sustain a management plan against sediment filling of the Ouizert dam at the basin outlet. The method enhancing the RUSLE model and confrontation with Google Earth can be easily adapted to other watersheds.

A Fast, Minimalist Search Tool for Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lynnes, C. S.; Macharrie, P. G.; Elkins, M.; Joshi, T.; Fenichel, L. H.

2005-12-01

We present a tool that emphasizes speed and simplicity in searching remotely sensed Earth Science data. The tool, nicknamed "Mirador" (Spanish for a scenic overlook), provides only four freetext search form fields, for Keywords, Location, Data Start and Data Stop. This contrasts with many current Earth Science search tools that offer highly structured interfaces in order to ensure precise, non-zero results. The disadvantages of the structured approach lie in its complexity and resultant learning curve, as well as the time it takes to formulate and execute the search, thus discouraging iterative discovery. On the other hand, the success of the basic Google search interface shows that many users are willing to forgo high search precision if the search process is fast enough to enable rapid iteration. Therefore, we employ several methods to increase the speed of search formulation and execution. Search formulation is expedited by the minimalist search form, with only one required field. Also, a gazetteer enables the use of geographic terms as shorthand for latitude/longitude coordinates. The search execution is accelerated by initially presenting dataset results (returned from a Google Mini appliance) with an estimated number of "hits" for each dataset based on the user's space-time constraints. The more costly file-level search is executed against a PostGres database only when the user "drills down", and then covering only the fraction of the time period needed to return the next page of results. The simplicity of the search form makes the tool easy to learn and use, and the speed of the searches enables an iterative form of data discovery.

Case study of visualizing global user download patterns using Google Earth and NASA World Wind

NASA Astrophysics Data System (ADS)

Zong, Ziliang; Job, Joshua; Zhang, Xuesong; Nijim, Mais; Qin, Xiao

2012-01-01

Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASA World Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the "hot spot" areas of research. Most importantly, our methods demonstrate an easy way to geo-visualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).

KML-Based Access and Visualization of High Resolution LiDAR Topography

NASA Astrophysics Data System (ADS)

Crosby, C. J.; Blair, J. L.; Nandigam, V.; Memon, A.; Baru, C.; Arrowsmith, J. R.

2008-12-01

Over the past decade, there has been dramatic growth in the acquisition of LiDAR (Light Detection And Ranging) high-resolution topographic data for earth science studies. Capable of providing digital elevation models (DEMs) more than an order of magnitude higher resolution than those currently available, LiDAR data allow earth scientists to study the processes that contribute to landscape evolution at resolutions not previously possible yet essential for their appropriate representation. These datasets also have significant implications for earth science education and outreach because they provide an accurate representation of landforms and geologic hazards. Unfortunately, the massive volume of data produced by LiDAR mapping technology can be a barrier to their use. To make these data available to a larger user community, we have been exploring the use of Keyhole Markup Language (KML) and Google Earth to provide access to LiDAR data products and visualizations. LiDAR digital elevation models are typically delivered in a tiled format that lends itself well to a KML-based distribution system. For LiDAR datasets hosted in the GEON OpenTopography Portal (www.opentopography.org) we have developed KML files that show the extent of available LiDAR DEMs and provide direct access to the data products. Users interact with these KML files to explore the extent of the available data and are able to select DEMs that correspond to their area of interest. Selection of a tile loads a download that the user can then save locally for analysis in their software of choice. The GEON topography system also has tools available that allow users to generate custom DEMs from LiDAR point cloud data. This system is powerful because it enables users to access massive volumes of raw LiDAR data and to produce DEM products that are optimized to their science applications. We have developed a web service that converts the custom DEM models produced by the system to a hillshade that is delivered to the user as a KML groundoverlay. The KML product enables users to quickly and easily visualize the DEMs in Google Earth. By combining internet-based LiDAR data processing with KML visualization products, users are able to execute computationally intensive data sub-setting, processing and visualization without having local access to computing resources, GIS software, or data processing expertise. Finally, GEON has partnered with the US Geological Survey to generate region-dependant network linked KML visualizations for large volumes of LiDAR derived hillshades of the Northern San Andreas fault system. These data, acquired by the NSF-funded GeoEarthScope project, offer an unprecedented look at active faults in the northern portion of the San Andreas system. Through the region-dependant network linked KML, users can seamlessly access 1 meter hillshades (both 315 and 45 degree sun angles) for the full 1400 square kilometer dataset, without downloading huge volumes of data. This type of data access has great utility for users ranging from earthquake scientists to K-12 educators who wish to introduce cutting edge real world data into their earth science lessons.

A Participatory Agent-Based Simulation for Indoor Evacuation Supported by Google Glass.

PubMed

Sánchez, Jesús M; Carrera, Álvaro; Iglesias, Carlos Á; Serrano, Emilio

2016-08-24

Indoor evacuation systems are needed for rescue and safety management. One of the challenges is to provide users with personalized evacuation routes in real time. To this end, this project aims at exploring the possibilities of Google Glass technology for participatory multiagent indoor evacuation simulations. Participatory multiagent simulation combines scenario-guided agents and humans equipped with Google Glass that coexist in a shared virtual space and jointly perform simulations. The paper proposes an architecture for participatory multiagent simulation in order to combine devices (Google Glass and/or smartphones) with an agent-based social simulator and indoor tracking services.

AFRC2016-0054-528

NASA Image and Video Library

2016-02-27

Sam Choi and Naiara Pinto observe Google Earth overlaid with in almost real time what the synthetic aperture radar is mapping from the C-20A aircraft. Researchers were in the sky and on the ground to take measurements of plant mass, distribution of trees, shrubs and ground cover and the diversity of plants and how much carbon is absorbed by them.

Smartphones and Time Zones

ERIC Educational Resources Information Center

Baird, William; Secrest, Jeffery; Padgett, Clifford; Johnson, Wayne; Hagrelius, Claire

2016-01-01

Using the Sun to tell time is an ancient idea, but we can take advantage of modern technology to bring it into the 21st century for students in astronomy, physics, or physical science classes. We have employed smartphones, Google Earth, and 3D printing to find the moment of local noon at two widely separated locations. By reviewing GPS…

Using Google Earth to Teach the Magnitude of Deep Time

ERIC Educational Resources Information Center

Parker, Joel D.

2011-01-01

Most timeline analogies of geologic and evolutionary time are fundamentally flawed. They trade off the problem of grasping very long times for the problem of grasping very short distances. The result is an understanding of relative time with little comprehension of absolute time. Earlier work has shown that the distances most easily understood by…

Applying Modern Stage Theory to Mauritania: A Prescription to Encourage Entrepreneurship

DTIC Science & Technology

2014-12-01

entrepreneurship, stage theory, development, Africa , factor-driven, trade freedom, business freedom 15. NUMBER OF PAGES 77 16. PRICE CODE 17...SOUTH ASIA, SUB-SAHARAN AFRICA ) from the NAVAL POSTGRADUATE SCHOOL December 2014 Author: Jennifer M. Warren Approved by: Robert E...Notes, Coins) .......................................................................... 4  Figure 2.  Satellite map of West Africa (from Google Earth

The World in Spatial Terms: Mapmaking and Map Reading

ERIC Educational Resources Information Center

Ekiss, Gale Olp; Trapido-Lurie, Barbara; Phillips, Judy; Hinde, Elizabeth

2007-01-01

Maps and mapping activities are essential in the primary grades. Maps are truly ubiquitous today, as evidenced by the popularity of websites such as Google Earth and Mapquest, and by devices such as Global Positioning System (GPS) units in cars, planes, and boats. Maps can give visual settings to travel stories and historical narratives and can…

A land cover change study in the Highlands of Northern Ethiopia using a flight of aerial photographs dating back to the 1930s

NASA Astrophysics Data System (ADS)

Guyassa, Etefa; Frankl, Amaury; Zenebe, Amanuel; Lanckriet, Sil; Demissie, Biadgilgn; Zenebe, Gebreyohanis; Poesen, Jean; Nyssen, Jan

2016-04-01

In the Highlands of Northern Ethiopia, land degradation is claimed to have occurred over a long time mainly due agricultural practices and lack of land management. However, quantitative information on the long term land use, cover and management change is rare. The knowledge of such historical changes is essential for the present and future land management for sustainable development, especially in an agriculture-based economy. Hence, this study aimed to investigate the changes of land use, cover and management around Hagere Selam, Northern Ethiopia, over the last 80 years (1935 - 2014). We recovered a flight of ten aerial photographs at an approximate scale of 1:11,500, realized by the Italian Military Geographical Institute in 1935, along a mountain ridge between 13.6490°N, 39.1848°E and 13.6785°N, 39.2658°E. Jointly with Google Earth images (2014), the historical aerial photographs were used to compare changes over the long time. The point-count technique was used by overlaying a grid of 18 x 15 points (small squares) on 20 cm x 15 cm aerial photographs and on Google Earth images representing the same area. Occurrence of major land cover types (cropland, forest, grassland, shrubland, bare land, built-up areas and water body) was counted to compute their proportion in 1935 and 2014. In 1935, cropland, shrubland and built-up areas were predominant while other land cover types were not observed. On the Google Earth images, all categories were observed except forest. The results show that in both times cropland was the dominant land cover followed by shrubland. The proportion of cropland at present (70.5%) is approximately the same as in the 1930s (72%), but shrubland decreased and bare land, grassland and built-up areas have increased. Hence, the large share of cropland was maintained over the past long period without allowing for woody vegetation to expand its area, while some cropland was abandoned and converted to grassland and bare land. The increased proportion of built-up areas also explains the shrinking of shrubland. On the studied flight of aerial photographs, forests were not existing in 1935 and have not been restored until present. The increased area of open water, on the other hand, is related to the ongoing land rehabilitation activities carried out in the region. These results confirm previous studies that severe land degradation has occurred in the Highlands of Northern Ethiopia over a long time, due to early (pre-1935) cropland expansion and deforestation.

Creating global comparative analyses of tectonic rifts, monogenetic volcanism and inverted relief

NASA Astrophysics Data System (ADS)

van Wyk de Vries, Benjamin

2016-04-01

I have been all around the world, and to other planets and have travelled from the present to the Archaean and back to seek out the most significant tectonic rifts, monogenetic volcanoes and examples of inverted relief. I have done this to provide a broad foundation of the comparative analysis for the Chaîne des Puys - Limagne fault nomination to UNESCO world Heritage. This would have been an impossible task, if not for the cooperation of the scientific community and for Google Earth, Google Maps and academic search engines. In preparing global comparisons of geological features, these quite recently developed tools provide a powerful way to find and describe geological features. The ability to do scientific crowd sourcing, rapidly discussing with colleagues about features, allows large numbers of areas to be checked and the open GIS tools (such as Google Earth) allow a standardised description. Search engines also allow the literature on areas to be checked and compared. I will present a comparative study of rifts of the world, monogenetic volcanic field and inverted relief, integrated to analyse the full geological system represented by the Chaîne des Puys - Limagne fault. The analysis confirms that the site is an exceptional example of the first steps of continental drift in a mountain rift setting, and that this is necessarily seen through the combined landscape of tectonic, volcanic and geomorphic features. The analysis goes further to deepen the understanding of geological systems and stresses the need for more study on geological heritage using such a global and broad systems approach.

The GLIMS Glacier Database

NASA Astrophysics Data System (ADS)

Raup, B. H.; Khalsa, S. S.; Armstrong, R.

2007-12-01

The Global Land Ice Measurements from Space (GLIMS) project has built a geospatial and temporal database of glacier data, composed of glacier outlines and various scalar attributes. These data are being derived primarily from satellite imagery, such as from ASTER and Landsat. Each "snapshot" of a glacier is from a specific time, and the database is designed to store multiple snapshots representative of different times. We have implemented two web-based interfaces to the database; one enables exploration of the data via interactive maps (web map server), while the other allows searches based on text-field constraints. The web map server is an Open Geospatial Consortium (OGC) compliant Web Map Server (WMS) and Web Feature Server (WFS). This means that other web sites can display glacier layers from our site over the Internet, or retrieve glacier features in vector format. All components of the system are implemented using Open Source software: Linux, PostgreSQL, PostGIS (geospatial extensions to the database), MapServer (WMS and WFS), and several supporting components such as Proj.4 (a geographic projection library) and PHP. These tools are robust and provide a flexible and powerful framework for web mapping applications. As a service to the GLIMS community, the database contains metadata on all ASTER imagery acquired over glacierized terrain. Reduced-resolution of the images (browse imagery) can be viewed either as a layer in the MapServer application, or overlaid on the virtual globe within Google Earth. The interactive map application allows the user to constrain by time what data appear on the map. For example, ASTER or glacier outlines from 2002 only, or from Autumn in any year, can be displayed. The system allows users to download their selected glacier data in a choice of formats. The results of a query based on spatial selection (using a mouse) or text-field constraints can be downloaded in any of these formats: ESRI shapefiles, KML (Google Earth), MapInfo, GML (Geography Markup Language) and GMT (Generic Mapping Tools). This "clip-and-ship" function allows users to download only the data they are interested in. Our flexible web interfaces to the database, which includes various support layers (e.g. a layer to help collaborators identify satellite imagery over their region of expertise) will facilitate enhanced analysis to be undertaken on glacier systems, their distribution, and their impacts on other Earth systems.

On the use of wavelet for extracting feature patterns from Multitemporal google earth satellite data sets

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2012-04-01

The great amount of multispectral VHR satellite images, even available free of charge in Google earth has opened new strategic challenges in the field of remote sensing for archaeological studies. These challenges substantially deal with: (i) the strategic exploitation of satellite data as much as possible, (ii) the setting up of effective and reliable automatic and/or semiautomatic data processing strategies and (iii) the integration with other data sources from documentary resources to the traditional ground survey, historical documentation, geophysical prospection, etc. VHR satellites provide high resolution data which can improve knowledge on past human activities providing precious qualitative and quantitative information developed to such an extent that currently they share many of the physical characteristics of aerial imagery. This makes them ideal for investigations ranging from a local to a regional scale (see. for example, Lasaponara and Masini 2006a,b, 2007a, 2011; Masini and Lasaponara 2006, 2007, Sparavigna, 2010). Moreover, satellite data are still the only data source for research performed in areas where aerial photography is restricted because of military or political reasons. Among the main advantages of using satellite remote sensing compared to traditional field archaeology herein we briefly focalize on the use of wavelet data processing for enhancing google earth satellite data with particular reference to multitemporal datasets. Study areas selected from Southern Italy, Middle East and South America are presented and discussed. Results obtained point out the use of automatic image enhancement can successfully applied as first step of supervised classification and intelligent data analysis for semiautomatic identification of features of archaeological interest. Reference Lasaponara R, Masini N (2006a) On the potential of panchromatic and multispectral Quickbird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological remains buried under vegetated and not vegetated areas. J Cult Heri 8 (1): 53-60. Sparavigna Enhancing the Google imagery using a wavelet filter, A.C. Sparavigna,http://arxiv.org/abs/1009.1590

Oyster Fisheries App

NASA Technical Reports Server (NTRS)

Perez Guerrero, Geraldo A.; Armstrong, Duane; Underwood, Lauren

2015-01-01

This project is creating a cloud-enabled, HTML 5 web application to help oyster fishermen and state agencies apply Earth science to improve the management of this important natural and economic resource. The Oyster Fisheries app gathers and analyzes environmental and water quality information, and alerts fishermen and resources managers about problems in oyster fishing waters. An intuitive interface based on Google Maps displays the geospatial information and provides familiar interactive controls to the users. Alerts can be tailored to notify users when conditions in specific leases or public fishing areas require attention. The app is hosted on the Amazon Web Services cloud. It is being developed and tested using some of the latest web development tools such as web components and Polymer.

The Palladiolibrary Geo-Models AN Open 3d Archive to Manage and Visualize Information-Communication Resources about Palladio

NASA Astrophysics Data System (ADS)

Apollonio, F. I.; Baldissini, S.; Clini, P.; Gaiani, M.; Palestini, C.; Trevisan, C.

2013-07-01

The paper describes objectives, methods, procedures and outcomes of the development of the digital archive of Palladio works and documentation: the PALLADIOLibrary of Centro Internazionale di Studi di Architettura Andrea Palladio di Vicenza (CISAAP). The core of the application consists of fifty-one reality-based 3D models usable and navigable within a system grounded on GoogleEarth. This information system, a collaboration of four universities bearers of specific skills returns a comprehensive, structured and coherent semantic interpretation of Palladian landscape through shapes realistically reconstructed from historical sources and surveys and treated for GE with Ambient Occlusion techniques, overcoming the traditional display mode.

Google Earth as a Vehicle to Integrating Multiple Layers of Environmental Satellite Data for Weather and Science Applications

NASA Astrophysics Data System (ADS)

Turk, F. J.; Miller, S. D.

2007-12-01

One of the main challenges facing current and future environmental satellite systems (e.g, the future National Polar Orbiting Environmental Satellite System (NPOESS)) is reaching and entraining the diverse user community via communication of how these systems address their particular needs. A necessary element to meeting this challenge is effective data visualization: facilitating the display, animation and layering of multiple satellite imaging and sounding sensors (providing complementary information) in a user-friendly and intuitive fashion. In light of the fact that these data are rapidly making their way into the classroom owing to efficient and timely data archival systems and dissemination over the Internet, there is a golden opportunity to leverage existing technology to introduce environmental science to wide spectrum of users. Google Earth's simplified interface and underlying markup language enables access to detailed global geographic information, and contains features which are both desirable and advantageous for geo-referencing and combining a wide range of environmental satellite data types. Since these satellite data are available with a variety of horizontal spatial resolutions (tens of km down to hundreds of meters), the imagery can be sub-setted (tiled) at a very small size. This allows low-bandwidth users to efficiently view and animate a sequence of imagery while zoomed out from the surface, whereas high-bandwidth users can efficiently zoom into the finest image resolution when viewing fine-scale phenomena such as fires, volcanic activity, as well as the details of meteorological phenomena such as hurricanes, rainfall, lightning, winds, etc. Dynamically updated network links allow for near real-time updates such that these data can be integrated with other Earth-hosted applications and exploited not only in the teaching environment, but also for operational users in the government and private industry sectors. To conceptualize how environmental satellite data would be utilized within a geobrowser in a near real-time setting, we present a demonstration from the 2007 hurricane season, developed within the Google Earth framework. A menu of imagery based sequential satellite overpasses (GOES and other geostationary satellites, TRMM, CloudSat, Terra, Aqua, DMSP, NOAA, QuikScat) during the storm lifecycle, are presented to the Earth client in an structured folder format. The remapping of these satellite data follows the hurricane track, enabling the user to view, animate, zoom, overlay and combine visible, infrared and passive microwave imagery and combine with other data (surface reports, forecasts, surface winds, ground and spaceborne radars, etc.) at various stages of the hurricane lifecycle. Pop-up balloons provide training that explains the properties and capabilities of the satellite datasets and what components of the underlying weather are represented. Future satellite overpass tracks are provided so that the user can anticipate imagery updates several days in advance (e.g., as a hurricane approaches landfall). This combination of geo-navigable data provides a convenient framework for efficiently demonstrating meteorological, oceanographic and weather and climate concepts to students, planners, and the public at large.

A Participatory Agent-Based Simulation for Indoor Evacuation Supported by Google Glass

PubMed Central

Sánchez, Jesús M.; Carrera, Álvaro; Iglesias, Carlos Á.; Serrano, Emilio

2016-01-01

Indoor evacuation systems are needed for rescue and safety management. One of the challenges is to provide users with personalized evacuation routes in real time. To this end, this project aims at exploring the possibilities of Google Glass technology for participatory multiagent indoor evacuation simulations. Participatory multiagent simulation combines scenario-guided agents and humans equipped with Google Glass that coexist in a shared virtual space and jointly perform simulations. The paper proposes an architecture for participatory multiagent simulation in order to combine devices (Google Glass and/or smartphones) with an agent-based social simulator and indoor tracking services. PMID:27563911

CEOS visualization environment (COVE) tool for intercalibration of satellite instruments

USGS Publications Warehouse

Kessler, P.D.; Killough, B.D.; Gowda, S.; Williams, B.R.; Chander, G.; Qu, Min

2013-01-01

Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of international and domestic space agencies and organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration planning efforts, whether those efforts require past, present, or future predictions. This paper provides a brief overview of the COVE tool, its validation, accuracies, and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments.

CEOS Visualization Environment (COVE) Tool for Intercalibration of Satellite Instruments

NASA Technical Reports Server (NTRS)

Kessler, Paul D.; Killough, Brian D.; Gowda, Sanjay; Williams, Brian R.; Chander, Gyanesh; Qu, Min

2013-01-01

Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of space agencies and of international and domestic organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration efforts. This paper provides a brief overview of the COVE tool, its validation, accuracies and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments.

Infodemiology of status epilepticus: A systematic validation of the Google Trends-based search queries.

PubMed

Bragazzi, Nicola Luigi; Bacigaluppi, Susanna; Robba, Chiara; Nardone, Raffaele; Trinka, Eugen; Brigo, Francesco

2016-02-01

People increasingly use Google looking for health-related information. We previously demonstrated that in English-speaking countries most people use this search engine to obtain information on status epilepticus (SE) definition, types/subtypes, and treatment. Now, we aimed at providing a quantitative analysis of SE-related web queries. This analysis represents an advancement, with respect to what was already previously discussed, in that the Google Trends (GT) algorithm has been further refined and correlational analyses have been carried out to validate the GT-based query volumes. Google Trends-based SE-related query volumes were well correlated with information concerning causes and pharmacological and nonpharmacological treatments. Google Trends can provide both researchers and clinicians with data on realities and contexts that are generally overlooked and underexplored by classic epidemiology. In this way, GT can foster new epidemiological studies in the field and can complement traditional epidemiological tools. Copyright © 2015 Elsevier Inc. All rights reserved.

Tracking changes of river morphology in Ayeyarwady River in Myanmar using earth observations and surface water mapping tool

NASA Astrophysics Data System (ADS)

Piman, T.; Schellekens, J.; Haag, A.; Donchyts, G.; Apirumanekul, C.; Hlaing, K. T.

2017-12-01

River morphology changes is one of the key issues in Ayeyarwady River in Myanmar which cause impacts on navigation, riverine habitats, agriculture lands, communities and livelihoods near the bank of the river. This study is aimed to track the changes in river morphology in the middle reach of Ayeyarwady River over last 30 years from 1984-2014 to improve understanding of riverbank dynamic, erosion and deposition procress. Earth observations including LandSat-7, LandSat-8, Digital Elevation Model from SRTM Plus and, ASTER-2 GoogleMap and Open Street Map were obtained for the study. GIS and remote sensing tools were used to analyze changes in river morphology while surface water mapping tool was applied to determine how the dynamic behaviour of the surface river and effect of river morphology changes. The tool consists of two components: (1) a Google Earth Engine (GEE) javascript or python application that performs image analysis and (2) a user-friendly site/app using Google's appspot.com that exposes the application to the users. The results of this study shown that the fluvial morphology in the middle reach of Ayeyarwady River is continuously changing under the influence of high water flows in particularly from extreme flood events and land use change from mining and deforestation. It was observed that some meandering sections of the riverbank were straightened, which results in the movement of sediment downstream and created new sections of meandering riverbank. Several large islands have formed due to the stabilization by vegetation and is enforced by sedimentation while many small bars were formed and migrated dynamically due to changes in water levels and flow velocity in the wet and dry seasons. The main channel was changed to secondary channel in some sections of the river. This results a constant shift of the navigation route. We also found that some villages were facing riverbank erosion which can force villagers to relocate. The study results demonstrated that the products from earth observations and the surface water mapping tool could detect dynamic changes of river morphology in the Ayeyarwady River. This information is useful to support navigation and riverbank protection planning and formulating mitigation measures for local communities that are affecting by riverbank erosion.

Addressing key concepts in physical geography through interactive learning activities in an online geo-ICT environment

NASA Astrophysics Data System (ADS)

Verstraeten, Gert; Steegen, An; Martens, Lotte

2016-04-01

The increasing number of geospatial datasets and free online geo-ICT tools offers new opportunities for education in Earth Sciences. Geospatial technology indeed provides an environment through which interactive learning can be introduced in Earth Sciences curricula. However, the effectiveness of such e-learning approaches in terms of learning outcomes has rarely been addressed. Here, we present our experience with the implementation of digital interactive learning activities within an introductory Physical Geography course attended by 90 undergraduate students in Geography, Geology, Biology and Archaeology. Two traditional lectures were replaced by interactive sessions (each 2 h) in a flexible classroom where students had to work both in team and individually in order to explore some key concepts through the integrated use of geospatial data within Google EarthTM. A first interactive lesson dealt with the classification of river systems and aimed to examine the conditions under which rivers tend to meander or to develop a braided pattern. Students were required to collect properties of rivers (river channel pattern, channel slope, climate, discharge, lithology, vegetation, etc). All these data are available on a global scale and have been added as separate map layers in Google EarthTM. Each student collected data for at least two rivers and added this information to a Google Drive Spreadsheet accessible to the entire group. This resulted in a database of more than one hundred rivers spread over various environments worldwide. In a second phase small groups of students discussed the potential relationships between river channel pattern and its controlling factors. Afterwards, the findings of each discussion group were presented to the entire audience. The same set-up was followed in a second interactive session to explore spatial variations in ecosystem properties such as net primary production and soil carbon content. The qualitative evaluation of both interactive sessions showed that the majority of students perceive these as very useful and inspiring. Students were more capable in exploring the spatial linkages between various environmental variables and processes compared to traditional lectures. Furthermore, the format of the sessions offered a forum in which undergraduate students from a variety of disciplines discussed the learning content in mixed groups. The success of interactive learning activities, however, strongly depends on the quality of the educational infrastructure (flexible spaces, wireless connections with sufficient broadband capacity).

Quantum Leap in Cartography as a requirement of Sustainable Development of the World

NASA Astrophysics Data System (ADS)

Tikunov, Vladimir S.; Tikunova, Iryna N.; Eremchenko, Eugene N.

2018-05-01

Sustainable development is one of the most important challenges for humanity and one of the priorities of the United Nations. Achieving sustainability of the whole World is a main goal of management at all levels - from personal to local to global. Therefore, decision making should be supported by relevant geospatial information system. Nevertheless, classical geospatial products, maps and GIS, violate fundamental demand of `situational awareness' concept, well-known philosophy of decision-making - same representation of situation within a same volume of time and space for all decision-makers. Basic mapping principles like generalization and projections split the universal single model of situation on number of different separate and inconsistent replicas. It leads to wrong understanding of situation and, after all - to incorrect decisions. In another words, quality of the sustainable development depends on effective decision-making support based on universal global scale-independent and projection-independent model. This new way for interacting with geospatial information is a quantum leap in cartography method. It is implemented in the so-called `Digital Earth' paradigm and geospatial services like Google Earth. Com-paring of both methods, as well as possibilities of implementation of Digital Earth in the sustain-able development activities, are discussed.

Post-Nor'Ida coastal oblique aerial photographs collected from Ocean City, Maryland, to Hatteras, North Carolina, December 4, 2009

USGS Publications Warehouse

Morgan, Karen L. M.; Krohn, M. Dennis; Guy, Kristy K.

2015-01-01

In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files.

Baseline Coastal Oblique Aerial Photographs Collected from Navarre Beach, Florida, to Breton Island, Louisiana, September 1, 2014

USGS Publications Warehouse

Morgan, Karen L. M.

2015-08-31

In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files.

Pinpointing Watershed Pollution on a Virtual Globe

ERIC Educational Resources Information Center

Saunders, Cheston; Taylor, Amy

2014-01-01

Pollution is not a problem we just read about anymore. It affects the air we breathe, the land we live on, and the water we consume. After noticing a lack of awareness in students, a lesson was developed that used Google Earth to pinpoint sources of pollution in the local area and in others across the country, and their effects on the surrounding…

Assessment of rainwater harvesting potential using GIS

NASA Astrophysics Data System (ADS)

Hari, Durgasrilakshmi; Ramamohan Reddy, K.; Vikas, Kola; Srinivas, N.; Vikas, G.

2018-03-01

Rainwater harvesting (RWH) is one of the best practices to overcome the scarcity of water. Rainwater harvesting involves collection and storage of rainwater locally through different technologies, for future use. It is also useful for livestock, groundwater recharge and for irrigation practices. Potential of rainwater harvesting refers to the capacity of an individual catchment that harnesses the water falling on the catchment during a particular year considering all rainy days. The present study deals with the identification of the study area boundary and marking it as a Polygon in Google Earth Pro Later, Rooftops of various house entities and roads were digitized using the Polygon command in Google Earth Pro. GIS technique is employed for locating boundaries of the study area and for calculating the areas of various types of rooftops and roads. With the application of GIS, it is possible to assess the total potential of water that can be harvested. The present study will enable us to identify the suitable type of water harvesting structure along with the number of structures required. It is extremely an ideal and effective solution to overcome the water crisis through water conservation in the study area.

Fast segmentation of satellite images using SLIC, WebGL and Google Earth Engine

NASA Astrophysics Data System (ADS)

Donchyts, Gennadii; Baart, Fedor; Gorelick, Noel; Eisemann, Elmar; van de Giesen, Nick

2017-04-01

Google Earth Engine (GEE) is a parallel geospatial processing platform, which harmonizes access to petabytes of freely available satellite images. It provides a very rich API, allowing development of dedicated algorithms to extract useful geospatial information from these images. At the same time, modern GPUs provide thousands of computing cores, which are mostly not utilized in this context. In the last years, WebGL became a popular and well-supported API, allowing fast image processing directly in web browsers. In this work, we will evaluate the applicability of WebGL to enable fast segmentation of satellite images. A new implementation of a Simple Linear Iterative Clustering (SLIC) algorithm using GPU shaders will be presented. SLIC is a simple and efficient method to decompose an image in visually homogeneous regions. It adapts a k-means clustering approach to generate superpixels efficiently. While this approach will be hard to scale, due to a significant amount of data to be transferred to the client, it should significantly improve exploratory possibilities and simplify development of dedicated algorithms for geoscience applications. Our prototype implementation will be used to improve surface water detection of the reservoirs using multispectral satellite imagery.

Mapping of Sample Collection Data: GIS Tools for the Natural Product Researcher

PubMed Central

Oberlies, Nicholas H.; Rineer, James I.; Alali, Feras Q.; Tawaha, Khaled; Falkinham, Joseph O.; Wheaton, William D.

2009-01-01

Scientists engaged in the research of natural products often either conduct field collections themselves or collaborate with partners who do, such as botanists, mycologists, or SCUBA divers. The information gleaned from such collecting trips (e.g. longitude/latitude coordinates, geography, elevation, and a multitude of other field observations) have provided valuable data to the scientific community (e.g., biodiversity), even if it is tangential to the direct aims of the natural products research, which are often focused on drug discovery and/or chemical ecology. Geographic Information Systems (GIS) have been used to display, manage, and analyze geographic data, including collection sites for natural products. However, to the uninitiated, these tools are often beyond the financial and/or computational means of the natural product scientist. With new, free, and easy-to-use geospatial visualization tools, such as Google Earth, mapping and geographic imaging of sampling data are now within the reach of natural products scientists. The goals of the present study were to develop simple tools that are tailored for the natural products setting, thereby presenting a means to map such information, particularly via open source software like Google Earth. PMID:20161345

The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth

NASA Astrophysics Data System (ADS)

Osborn, T. J.; Jones, P. D.

2014-02-01

The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

HydroViz: design and evaluation of a Web-based tool for improving hydrology education

NASA Astrophysics Data System (ADS)

Habib, E.; Ma, Y.; Williams, D.; Sharif, H. O.; Hossain, F.

2012-10-01

HydroViz is a Web-based, student-centered, educational tool designed to support active learning in the field of Engineering Hydrology. The design of HydroViz is guided by a learning model that is based on learning with data and simulations, using real-world natural hydrologic systems to convey theoretical concepts, and using Web-based technologies for dissemination of the hydrologic education developments. This model, while being used in a hydrologic education context, can be adapted in other engineering educational settings. HydroViz leverages the free Google Earth resources to enable presentation of geospatial data layers and embed them in web pages that have the same look and feel of Google Earth. These design features significantly facilitate the dissemination and adoption of HydroViz by any interested educational institutions regardless of their access to data or computer models. To facilitate classroom usage, HydroViz is populated with a set of course modules that can be used incrementally within different stages of an engineering hydrology curriculum. A pilot evaluation study was conducted to determine the effectiveness of the HydroViz tool in delivering its educational content, to examine the buy-in of the program by faculty and students, and to identify specific project components that need to be further pursued and improved. A total of 182 students from seven freshmen and senior-level undergraduate classes in three universities participated in the study. HydroViz was effective in facilitating students' learning and understanding of hydrologic concepts and increasing related skills. Students had positive perceptions of various features of HydroViz and they believe that HydroViz fits well in the curriculum. In general, HydroViz tend to be more effective with students in senior-level classes than students in freshmen classes. Lessons gained from this pilot study provide guidance for future adaptation and expansion studies to scale-up the application and utility of HydroViz and other similar systems into various hydrology and water-resource engineering curriculum settings. The paper presents a set of design principles that contribute to the development of other active hydrology educational systems.

Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential

USGS Publications Warehouse

Parks, Sean; Holsinger, Lisa M.; Voss, Morgan; Loehman, Rachel A.; Robinson, Nathaniel P.

2018-01-01

Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre-and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: the delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection and instead use a mean compositing approach in which all valid pixels (e.g. cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared the standard approach in which one pre-fire and post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western US. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets show improved validation statistics compared to parallel versions in which only one pre-fire and post-fire scene are used. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e. fire perimeters) be produced independently and prior to applying our methods, we suggest our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

New Tools for Viewing Spectrally and Temporally-Rich Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Bradley, E. S.; Toomey, M. P.; Roberts, D. A.; Still, C. J.

2010-12-01

High frequency, temporally extensive remote sensing datasets (GOES: 30 minutes, Santa Cruz Island webcam: nearly 5 years at every 10 min.) and airborne imaging spectrometry (AVIRIS with 224 spectral bands), present exciting opportunities for education, synthesis, and analysis. However, the large file volume / size can make holistic review and exploration difficult. In this research, we explore two options for visualization (1) a web-based portal for time-series analysis, PanOpt, and (2) Google Earth-based timestamped image overlays. PanOpt is an interactive website (http://zulu.geog.ucsb.edu/panopt/), which integrates high frequency (GOES) and multispectral (MODIS) satellite imagery with webcam ground-based repeat photography. Side-by-side comparison of satellite imagery with webcam images supports analysis of atmospheric and environmental phenomena. In this proof of concept, we have integrated four years of imagery for a multi-view FogCam on Santa Cruz Island off the coast of Southern California with two years of GOES-11 and four years of MODIS Aqua imagery subsets for the area (14,000 km2). From the PHP-based website, users can search the data (date, time of day, etc.) and specify timestep and display size; and then view the image stack as animations or in a matrix form. Extracted metrics for regions of interest (ROIs) can be viewed in different formats, including time-series and scatter plots. Through click and mouseover actions over the hyperlink-enabled data points, users can view the corresponding images. This directly melds the quantitative and qualitative aspects and could be particularly effective for both education as well as anomaly interpretation. We have also extended this project to Google Earth with timestamped GOES and MODIS image overlays, which can be controlled using the temporal slider and linked to a screen chart of ancillary meteorological data. The automated ENVI/IDL script for generating KMZ overlays was also applied for generating same-day visualization of AVIRIS acquisitions as part of the Gulf of Mexico oil spill response. This supports location-focused imagery review and synthesis, which is critical for successfully imaging moving targets, such as oil slicks.

Windows Into the Real World From a Virtual Globe

NASA Astrophysics Data System (ADS)

Rich, J.; Urban-Rich, J.

2007-12-01

Virtual globes such as Google Earth can be great tools for learning about the geographical variation of the earth. The key to virtual globes is the use of satellite imagery to provide a highly accurate view of the earth's surface. However, because the images are not updated regularly, variations in climate and vegetation over time can not be easily seen. In order to enhance the view of the earth and observe these changes by region and over time we are working to add near real time "windows" into the real world from a virtual globe. For the past 4 years we have been installing web cameras in areas of the world that will provide long term monitoring of global changes. By archiving hourly images from arctic, temperate and tropical regions we are creating a visual data set that is already beginning to tell the story of climate variability. The cameras are currently installed in 10 elementary schools in 3 countries and show the student's view out each window. The Windows Around the World program (http://www.WindowsAroundTheWorld.org) uses the images from these cameras to help students gain a better understanding of earth process and variability in climate and vegetation between different regions and over time. Previously we have used standard web based technologies such as DHTML and AJAX to provide near real-time access to these images and also provide enhanced functionality such as dynamic time lapse movies that allow users to see changes over months, days or hours up to the current hour (http://www.windowsaroundtheworld.org/north_america.aspx). We have integrated the camera images from Windows Around the World into Google Earth. Through network links and models we are creating a way for students to "fly" to another school in the program and see what the current view is out the window. By using a model as a screen, the image can be viewed from the same direction as the students who are sitting in a classroom at the participating school. Once at the school, visiting students can move around the area in three dimensions and gain a better understanding of what they are seeing out the window. Currently time-lapse images can be viewed at a lower resolution for all schools on the globe or when flying into an individual school, higher resolution time-lapse images can be seen. The observation of shadows, precipitation, movement of the sun and changes in vegetation allows the viewer to gain a better understanding of how the earth works and how the environment changes between regions and over time. World.org

Croatian Medical Journal citation score in Web of Science, Scopus, and Google Scholar.

PubMed

Sember, Marijan; Utrobicić, Ana; Petrak, Jelka

2010-04-01

To analyze the 2007 citation count of articles published by the Croatian Medical Journal in 2005-2006 based on data from the Web of Science, Scopus, and Google Scholar. Web of Science and Scopus were searched for the articles published in 2005-2006. As all articles returned by Scopus were included in Web of Science, the latter list was the sample for further analysis. Total citation counts for each article on the list were retrieved from Web of Science, Scopus, and Google Scholar. The overlap and unique citations were compared and analyzed. Proportions were compared using chi(2)-test. Google Scholar returned the greatest proportion of articles with citations (45%), followed by Scopus (42%), and Web of Science (38%). Almost a half (49%) of articles had no citations and 11% had an equal number of identical citations in all 3 databases. The greatest overlap was found between Web of Science and Scopus (54%), followed by Scopus and Google Scholar (51%), and Web of Science and Google Scholar (44%). The greatest number of unique citations was found by Google Scholar (n=86). The majority of these citations (64%) came from journals, followed by books and PhD theses. Approximately 55% of all citing documents were full-text resources in open access. The language of citing documents was mostly English, but as many as 25 citing documents (29%) were in Chinese. Google Scholar shares a total of 42% citations returned by two others, more influential, bibliographic resources. The list of unique citations in Google Scholar is predominantly journal based, but these journals are mainly of local character. Citations received by internationally recognized medical journals are crucial for increasing the visibility of small medical journals but Google Scholar may serve as an alternative bibliometric tool for an orientational citation insight.

A pan-Arctic Assessment of Hydraulic Geometry

NASA Astrophysics Data System (ADS)

Chen, H. Z. D.; Gleason, C. J.

2016-12-01

Arctic Rivers are a crucial part of the global hydrologic cycle, especially as our climate system alters toward an uncertain future. These rivers have many ecological and societal functions, such as funneling meltwater to the ocean and act as critical winter transport for arctic communities. Despite this importance, their fluvial geomorphology, in particular their hydraulic geometry (HG) is not fully understood due to their often remote locations. HG, including at-a-station (AHG), downstream (DHG), and the recently discovered At-many-stations (AMHG), provides the empirical basis between gauging measurements and how rivers respond to varying flow conditions, serving as an indicator to the critical functions mentioned above. Hence, a systematic cataloging of the AHG, DHG, and AMHG, of Arctic rivers is needed for a pan-Arctic view of fluvial geomorphic behavior. This study will document the width-based AHG, DHG, and AMHG for rivers wider than 120m with an Arctic Ocean drainage and gauge data with satellite records. First, we will make time-series width measurements from classified imagery at locations along all such rivers from Landsat archive since 1984, accessed within the Google Earth Engine cloud computing environment. Second, we will run available gauge data for width-based AHG, DHG, and AMHG over large river reaches. Lastly, we will assess these empirical relationships, seek regional trends, and changes in HG over time as climate change has on the Arctic system. This is part of an ongoing process in the larger scope of data calibration/validation for the Surface Water and Ocean Topography (SWOT) satellite planned for 2020, and HG mapping will aid the selection of field validation sites. The work showcase an unprecedented opportunity to process and retrieve scientifically significant HG data in the often inaccessible Arctic via Google Earth Engine. This unique platform makes such broad scale study possible, providing a blueprint for future large-area HG research.

Monitoring of coalbed water retention ponds in the Powder River Basin using Google Earth images and an Unmanned Aircraft System

NASA Astrophysics Data System (ADS)

Zhou, X.; Zhou, Z.; Apple, M. E.; Spangler, L.

2016-12-01

To extract methane from unminable seams of coal in the Powder River Basin of Montana and Wyoming, coalbed methane (CBM) water has to be pumped and kept in retention ponds rather than discharged to the vadose zone to mix with the ground water. The water areal coverage of these ponds changes due to evaporation and repetitive refilling. The water quality also changes due to growing of microalgae (unicellular or filamentous including green algae and diatoms), evaporation, and refilling. To estimate the water coverage changes and monitor water quality becomes important for monitoring the CBM water retention ponds to provide timely management plan for the newly pumped CBM water. Conventional methods such as various water indices based on multi-spectral satellite data such as Landsat because of the small pond size ( 100mx100m scale) and low spatial resolution ( 30m scale) of the satellite data. In this study we will present new methods to estimate water coverage and water quality changes using Google Earth images and images collected from an unmanned aircraft system (UAS) (Phantom 2 plus). Because these images have only visible bands (red, green, and blue bands), the conventional water index methods that involve near-infrared bands do not work. We design a new method just based on the visible bands to automatically extract water pixels and the intensity of the water pixel as a proxy for water quality after a series of image processing such as georeferencing, resampling, filtering, etc. Differential GPS positions along the water edges were collected the same day as the images collected from the UAS. Area of the water area was calculated from the GPS positions and used for the validation of the method. Because of the very high resolution ( 10-30 cm scale), the water areal coverage and water quality distribution can be accurately estimated. Since the UAS can be flied any time, water area and quality information can be collected timely.

Google glass-based remote control of a mobile robot

NASA Astrophysics Data System (ADS)

Yu, Song; Wen, Xi; Li, Wei; Chen, Genshe

2016-05-01

In this paper, we present an approach to remote control of a mobile robot via a Google Glass with the multi-function and compact size. This wearable device provides a new human-machine interface (HMI) to control a robot without need for a regular computer monitor because the Google Glass micro projector is able to display live videos around robot environments. In doing it, we first develop a protocol to establish WI-FI connection between Google Glass and a robot and then implement five types of robot behaviors: Moving Forward, Turning Left, Turning Right, Taking Pause, and Moving Backward, which are controlled by sliding and clicking the touchpad located on the right side of the temple. In order to demonstrate the effectiveness of the proposed Google Glass-based remote control system, we navigate a virtual Surveyor robot to pass a maze. Experimental results demonstrate that the proposed control system achieves the desired performance.

Space-Based Identification of Archaeological Illegal Excavations and a New Automatic Method for Looting Feature Extraction in Desert Areas

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Masini, Nicola

2018-06-01

The identification and quantification of disturbance of archaeological sites has been generally approached by visual inspection of optical aerial or satellite pictures. In this paper, we briefly summarize the state of the art of the traditionally satellite-based approaches for looting identification and propose a new automatic method for archaeological looting feature extraction approach (ALFEA). It is based on three steps: the enhancement using spatial autocorrelation, unsupervised classification, and segmentation. ALFEA has been applied to Google Earth images of two test areas, selected in desert environs in Syria (Dura Europos), and in Peru (Cahuachi-Nasca). The reliability of ALFEA was assessed through field surveys in Peru and visual inspection for the Syrian case study. Results from the evaluation procedure showed satisfactory performance from both of the two analysed test cases with a rate of success higher than 90%.

Evolution of errors in the altimetric bathymetry model used by Google Earth and GEBCO

NASA Astrophysics Data System (ADS)

Marks, K. M.; Smith, W. H. F.; Sandwell, D. T.

2010-09-01

We analyze errors in the global bathymetry models of Smith and Sandwell that combine satellite altimetry with acoustic soundings and shorelines to estimate depths. Versions of these models have been incorporated into Google Earth and the General Bathymetric Chart of the Oceans (GEBCO). We use Japan Agency for Marine-Earth Science and Technology (JAMSTEC) multibeam surveys not previously incorporated into the models as "ground truth" to compare against model versions 7.2 through 12.1, defining vertical differences as "errors." Overall error statistics improve over time: 50th percentile errors declined from 57 to 55 to 49 m, and 90th percentile errors declined from 257 to 235 to 219 m, in versions 8.2, 11.1 and 12.1. This improvement is partly due to an increasing number of soundings incorporated into successive models, and partly to improvements in the satellite gravity model. Inspection of specific sites reveals that changes in the algorithms used to interpolate across survey gaps with altimetry have affected some errors. Versions 9.1 through 11.1 show a bias in the scaling from gravity in milliGals to topography in meters that affected the 15-160 km wavelength band. Regionally averaged (>160 km wavelength) depths have accumulated error over successive versions 9 through 11. These problems have been mitigated in version 12.1, which shows no systematic variation of errors with depth. Even so, version 12.1 is in some respects not as good as version 8.2, which employed a different algorithm.

Monitoring Earth's reservoir and lake dynamics from space

NASA Astrophysics Data System (ADS)

Donchyts, G.; Eilander, D.; Schellekens, J.; Winsemius, H.; Gorelick, N.; Erickson, T.; Van De Giesen, N.

2016-12-01

Reservoirs and lakes constitute about 90% of the Earth's fresh surface water. They play a major role in the water cycle and are critical for the ever increasing demands of the world's growing population. Water from reservoirs is used for agricultural, industrial, domestic, and other purposes. Current digital databases of lakes and reservoirs are scarce, mainly providing only descriptive and static properties of the reservoirs. The Global Reservoir and Dam (GRanD) database contains almost 7000 entries while OpenStreetMap counts more than 500 000 entries tagged as a reservoir. In the last decade several research efforts already focused on accurate estimates of surface water dynamics, mainly using satellite altimetry, However, currently they are limited only to less than 1000 (mostly large) water bodies. Our approach is based on three main components. Firstly, a novel method, allowing automated and accurate estimation of surface area from (partially) cloud-free optical multispectral or radar satellite imagery. The algorithm uses satellite imagery measured by Landsat, Sentinel and MODIS missions. Secondly, a database to store reservoir static and dynamic parameters. Thirdly, a web-based tool, built on top of Google Earth Engine infrastructure. The tool allows estimation of surface area for lakes and reservoirs at planetary-scale at high spatial and temporal resolution. A prototype version of the method, database, and tool will be presented as well as validation using in-situ measurements.

GIS Database and Google Map of the Population at Risk of Cholangiocarcinoma in Mueang Yang District, Nakhon Ratchasima Province of Thailand.

PubMed

Kaewpitoon, Soraya J; Rujirakul, Ratana; Joosiri, Apinya; Jantakate, Sirinun; Sangkudloa, Amnat; Kaewthani, Sarochinee; Chimplee, Kanokporn; Khemplila, Kritsakorn; Kaewpitoon, Natthawut

2016-01-01

Cholangiocarcinoma (CCA) is a serious problem in Thailand, particularly in the northeastern and northern regions. Database of population at risk are need required for monitoring, surveillance, home health care, and home visit. Therefore, this study aimed to develop a geographic information system (GIS) database and Google map of the population at risk of CCA in Mueang Yang district, Nakhon Ratchasima province, northeastern Thailand during June to October 2015. Populations at risk were screened using the Korat CCA verbal screening test (KCVST). Software included Microsoft Excel, ArcGIS, and Google Maps. The secondary data included the point of villages, sub-district boundaries, district boundaries, point of hospital in Mueang Yang district, used for created the spatial databese. The populations at risk for CCA and opisthorchiasis were used to create an arttribute database. Data were tranfered to WGS84 UTM ZONE 48. After the conversion, all of the data were imported into Google Earth using online web pages www.earthpoint.us. Some 222 from a 4,800 population at risk for CCA constituted a high risk group. Geo-visual display available at following www.google.com/maps/d/u/0/ edit?mid=zPxtcHv_iDLo.kvPpxl5mAs90 and hl=th. Geo-visual display 5 layers including: layer 1, village location and number of the population at risk for CCA; layer 2, sub-district health promotion hospital in Mueang Yang district and number of opisthorchiasis; layer 3, sub-district district and the number of population at risk for CCA; layer 4, district hospital and the number of population at risk for CCA and number of opisthorchiasis; and layer 5, district and the number of population at risk for CCA and number of opisthorchiasis. This GIS database and Google map production process is suitable for further monitoring, surveillance, and home health care for CCA sufferers.

Exploring Sustainability Using images from Space

NASA Astrophysics Data System (ADS)

Chen, Loris; Salmon, Jennifer; Burns, Courtney

2016-04-01

Sustainability is the integrating theme of grade 8 science at Dwight D. Eisenhower in Wyckoff, New Jersey. With a focus on science, technology, engineering, and mathematics (STEM), sustainability establishes relevance for students, connects course work to current news topics, and ties together trimester explorations of earth science, physical science, and life science. Units are organized as problem-based learning units centered on disciplinary core ideas. Sustainability education empowers students to think about human and natural systems on a broader scale as they collaboratively seek solutions to scientific or engineering problems. The STEM-related sustainability issues encompass both global and local perspectives. Through problem solving, students acquire and demonstrate proficiency in the three-dimensions of Next Generation Science Standards (disciplinary core ideas, science and engineering practices, and crosscutting concepts). During the earth science trimester, students explore causes, effects, and mitigation strategies associated with urban heat islands and climate change. As a transition to a trimester of chemistry (physical science), students investigate the sustainability of mobile phone technology from raw materials mining to end-of-life disposal. Students explore natural resource conservation strategies in the interdisciplinary context of impacts on the economy, society, and environment. Sustainability creates a natural context for chemical investigations of ocean-atmosphere interactions such as ocean acidification. Students conclude the eighth grade with an investigation of heredity and evolution. Sustainability challenges embedded in genetics studies include endangered species management (California condors) and predicting the effects of climate change on populations in specific environments (Arctic and Antarctic regions). At Dwight D. Eisenhower Middle School, science students have access to a variety of web-enabled devices (e.g., Chromebooks, laptops, iPads). As a result, web-based resources are incorporated into student learning on a daily basis. This has created a truly global classroom for students who, via the Internet, can and do access materials from any country in the world. Students work collaboratively using Google Classroom and a suite of Google apps. Teacher-created websites serve as the textbook with text, video, static images, interactive images, and external links designed to stimulate student growth in scientific literacy, language arts, and mathematics. Images of Earth's systems generated from data collected by Earth orbiting spacecraft are essential tools for understanding sustainability concepts at global, national, regional, and local scales. Images and supporting data from NASA (U.S.), ESA (Europe), and JAXA (Japan) are used to explore Earth's atmosphere, hydrosphere, and geosphere. Simulations, time-lapses, and graphical representations of historical and real-time, remote-sensing data stimulate student questions and engage students in learning as they design and test models to explain complex interactions of Earth's systems and feedback loops between natural and human-made environments. As students make meaning of observations and communicate their perceptions and understandings to a variety of audiences, they gain mastery of scientific literacy, language arts skills, and mathematics skills.

Interactive Volcano Studies and Education Using Virtual Globes

NASA Astrophysics Data System (ADS)

Dehn, J.; Bailey, J. E.; Webley, P.

2006-12-01

Internet-based virtual globe programs such as Google Earth provide a spatial context for visualization of monitoring and geophysical data sets. At the Alaska Volcano Observatory, Google Earth is being used to integrate satellite imagery, modeling of volcanic eruption clouds and seismic data sets to build new monitoring and reporting tools. However, one of the most useful information sources for environmental monitoring is under utilized. Local populations, who have lived near volcanoes for decades are perhaps one of the best gauges for changes in activity. Much of the history of the volcanoes is only recorded through local legend. By utilizing the high level of internet connectivity in Alaska, and the interest of secondary education in environmental science and monitoring, it is proposed to build a network of observation nodes around local schools in Alaska and along the Aleutian Chain. A series of interactive web pages with observations on a volcano's condition, be it glow at night, puffs of ash, discolored snow, earthquakes, sounds, and even current weather conditions can be recorded, and the users will be able to see their reports in near real time. The database will create a KMZ file on the fly for upload into the virtual globe software. Past observations and legends could be entered to help put a volcano's long-term activity in perspective. Beyond the benefit to researchers and emergency managers, students and teachers in the rural areas will be involved in volcano monitoring, and gain an understanding of the processes and hazard mitigation efforts in their community. K-12 students will be exposed to the science, and encouraged to participate in projects at the university. Infrastructure at the university can be used by local teachers to augment their science programs, hopefully encouraging students to continue their education at the university level.

Using GIS and Google Earth for the creation of the Going-to-the-Sun Road Avalanche Atlas, Glacier National Park, Montana, USA

USGS Publications Warehouse

Peitzsch, Erich H.; Fagre, Daniel B.; Dundas, Mark

2010-01-01

Snow avalanche paths are key geomorphologic features in Glacier National Park, Montana, and an important component of mountain ecosystems: they are isolated within a larger ecosystem, they are continuously disturbed, and they contain unique physical characteristics (Malanson and Butler, 1984). Avalanches impact subalpine forest structure and function, as well as overall biodiversity (Bebi et al., 2009). Because avalanches are dynamic phenomena, avalanche path geometry and spatial extent depend upon climatic regimes. The USGS/GNP Avalanche Program formally began in 2003 as an avalanche forecasting program for the spring opening of the ever-popular Going-to-the-Sun Road (GTSR), which crosses through 37 identified avalanche paths. Avalanche safety and forecasting is a necessary part of the GTSR spring opening procedures. An avalanche atlas detailing topographic parameters and oblique photographs was completed for the GTSR corridor in response to a request from GNP personnel for planning and resource management. Using ArcMap 9.2 GIS software, polygons were created for every avalanche path affecting the GTSR using aerial imagery, field-based observations, and GPS measurements of sub-meter accuracy. Spatial attributes for each path were derived within the GIS. Resulting products include an avalanche atlas book for operational use, a geoPDF of the atlas, and a Google Earth flyover illustrating each path and associated photographs. The avalanche atlas aids park management in worker safety, infrastructure planning, and natural resource protection by identifying avalanche path patterns and location. The atlas was created for operational and planning purposes and is also used as a foundation for research such as avalanche ecology projects and avalanche path runout modeling.

Using Google AdWords in the MBA MIS Course

ERIC Educational Resources Information Center

Rosso, Mark A.; McClelland, Marilyn K.; Jansen, Bernard J.; Fleming, Sundar W.

2009-01-01

From February to June 2008, Google ran its first ever student competition in sponsored Web search, the 2008 Google Online Marketing Challenge (GOMC). The 2008 GOMC was based on registrations from 61 countries: 629 course sections from 468 universities participated, fielding over 4000 student teams of approximately 21,000 students. Working with a…

Stories from dynamic Earth: developing your sense of place through Landsat-based citizen science

NASA Astrophysics Data System (ADS)

Nelson, P.; Kennedy, R. E.; Nolin, A. W.; Hughes, J.; Bianchetti, R. A.; O'Connell, K.; Morrell, P.

2016-12-01

Many citizen science activities provide opportunities to understand a specific location on Earth at human scale and to collect local ecological knowledge that can improve the scientific endeavor of monitoring Earth. However, it can be challenging to comprehend ecological changes occurring at larger spatial and temporal scales. Based on the results of two professional development workshops designed for Oregon middle school science teachers in 2011-2013 and 2013-2016, we describe how working with multi-decade Landsat imagery transformed participants and students. Collaborating with scientists, the teachers used 30 years of time-series Landsat imagery with LandTrendr and IceTrendr algorithms to distill several study sites in Oregon, Washington, and Alaska (U.S) into periods of consistent long or short-duration landscape dynamics (e.g. stable areas, forestry activities, flooding, urbanization, tree growth). Using the spatial, tabular, and graphic outputs from this process, the teachers created climate change curriculum aligned to state and national standards. Web-enabled visualization tools, such as Google Earth, provided a platform that engaged students in understanding the drivers of their local landscape changes. Students and teachers reported increased interest in and understanding of their landscape. In addition to fulfilling classroom needs, the activities contributed data used in regional carbon modeling and land cover monitoring throughout California, Oregon, and Washington (U.S). We will discuss strategies and challenges to translating expert-level scientific data, models, methods, vocabulary, and conclusions into citizen science materials that support place-based climate change education across age ranges and educational disciplines. Finally, we share ways you can deepen your own sense of place while participating in citizen science activities that improve land cover and land use monitoring at local, regional, and global scales.

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis.

PubMed

Sharpe, J Danielle; Hopkins, Richard S; Cook, Robert L; Striley, Catherine W

2016-10-20

Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC's change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package "bcp" version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed.

Baseline coastal oblique aerial photographs collected from the Virginia/North Carolina border to Montauk Point, New York, October 5-6, 2014

USGS Publications Warehouse

Morgan, Karen L. M.

2015-10-02

In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files.

Visualization of Wind Data on Google Earth for the Three-dimensional Wind Field (3DWF) Model

DTIC Science & Technology

2012-09-01

ActiveX components or XPCOM extensions can be used by JavaScript to write data to the local file system. Since there is an inherent risk, it is very...important to only use these types of objects ( ActiveX or XPCOM) from a trusted source in order to minimize the exposure of a computer system to malware

Tactical Level Commander and Staff Toolkit

DTIC Science & Technology

2010-01-01

Sites Geodata.gov (for maps) http://gos2.geodata.gov Google Earth for .mil (United States Army Corps of Engineers (USACE) site) https...the eyes, ears, head, hands, back, and feet. When appropriate, personnel should wear protective lenses, goggles, or face shields . Leaders should...Typical hurricanes are about 300 miles wide, although they can vary considerably. Size is not necessarily an indication of hurricane intensity. The

From Geocaching to Virtual Reality: Technology tools that can transform courses into interactive learning expeditions

NASA Astrophysics Data System (ADS)

Moysey, S. M.; Lazar, K.; Boyer, D. M.; Mobley, C.; Sellers, V.

2016-12-01

Transforming classrooms into active learning environments is a key challenge in introductory-level courses. The technology explosion over the last decade, from the advent of mobile devices to virtual reality, is creating innumerable opportunities to engage students within and outside of traditional classroom settings. In particular, technology can be an effective tool for providing students with field experiences that would otherwise be logistically difficult in large, introductory earth science courses. For example, we have created an integrated platform for mobile devices using readily accessible "off the shelf" components (e.g., Google Apps, Geocaching.com, and Facebook) that allow individual students to navigate to geologically relevant sites, perform and report on activities at these locations, and share their findings through social media by posting "geoselfies". Students compete with their friends on a leaderboard, while earning incentives for completing extracurricular activities in courses. Thus in addition to exposing students to a wider range of meaningful and accessible geologic field experiences, they also build a greater sense of community and identity within the context of earth science classrooms. Rather than sending students to the field, we can also increasingly bring the field to students in classrooms using virtual reality. Ample mobile platforms are emerging that easily allow for the creation, curation, and viewing of photospheres (i.e., 360o images) with mobile phones and low-cost headsets; Google Street View, Earth, and Expeditions are leading the way in terms of ease of content creation and implementation in the classroom. While these tools are an excellent entry point to show students real-world sites, they currently lack the capacity for students to interact with the environment. We have therefore also developed an immersive virtual reality game that allows students to study the geology of the Grand Canyon using their smartphone and Google Cardboard viewer. Students navigate the terrain, collect rock samples, and investigate outcrops using a variety of tests and comparative analyses built into the game narrative. To enhance the realism of the game, real-world samples and outcrops from the Grand Canyon were scanned and embedded within the VR environment.

Development of Visualizations and Loggable Activities for the Geosciences. Results from Recent TUES Sponsored Projects

NASA Astrophysics Data System (ADS)

De Paor, D. G.; Bailey, J. E.; Whitmeyer, S. J.

2012-12-01

Our TUES research centers on the role of digital data, visualizations, animations, and simulations in undergraduate geoscience education. Digital hardware (smartphones, tablets, GPSs, GigaPan robotic camera mounts, etc.) are revolutionizing field data collection. Software products (GIS, 3-D scanning and modeling programs, virtual globes, etc.) have truly transformed the way geoscientists teach, learn, and do research. Whilst Google-Earth-style visualizations are famously user-friend for the person browsing, they can be notoriously unfriendly for the content creator. Therefore, we developed tools to help educators create and share visualizations as easily as if posting on Facebook. Anyone whoIf you wish to display geological cross sections on Google Earth, go to digitalplanet.org, upload image files, position them on a line of section, and share with the world through our KMZ hosting service. Other tools facilitate screen overlay and 3-D map symbol generation. We advocate use of such technology to enable undergraduate students to 'publish' their first mapping efforts even while they are working in the field. A second outcome of our TUES projects merges Second-Life-style interaction with Google Earth. We created games in which students act as first responders for natural hazard mitigation, prospectors for natural resource explorations, and structural geologist for map-making. Students are represented by avatars and collaborate by exchange of text messages - the natural mode of communication for the current generation. Teachers view logs showing student movements as well as transcripts of text messages and can scaffold student learning and geofence students to prevent wandering. Early results of in-class testing show positive learning outcomes. The third aspect of our program emphasizes dissemination. Experience shows that great effort is required to overcome activation energy and ensure adoption of new technology into the curriculum. We organized a GSA Penrose Conference, a GSA Pardee Keynote Symposium, and AGU Townhall Meeting, and numerous workshops at annual and regional meetings, and set up a web site dedicated to dissemination of program products. Future plans include development of augmented reality teaching resources, hosting of community mapping services, and creation of a truly 4-D virtual globe.;

Google Earth Visualizations of the Marine Automatic Identification System (AIS): Monitoring Ship Traffic in National Marine Sanctuaries

NASA Astrophysics Data System (ADS)

Schwehr, K.; Hatch, L.; Thompson, M.; Wiley, D.

2007-12-01

The Automatic Identification System (AIS) is a new technology that provides ship position reports with location, time, and identity information without human intervention from ships carrying the transponders to any receiver listening to the broadcasts. In collaboration with the USCG's Research and Development Center, NOAA's Stellwagen Bank National Marine Sanctuary (SBNMS) has installed 3 AIS receivers around Massachusetts Bay to monitor ship traffic transiting the sanctuary and surrounding waters. The SBNMS and the USCG also worked together propose the shifting the shipping lanes (termed the traffic separation scheme; TSS) that transit the sanctuary slightly to the north to reduce the probability of ship strikes of whales that frequent the sanctuary. Following approval by the United Nation's International Maritime Organization, AIS provided a means for NOAA to assess changes in the distribution of shipping traffic caused by formal change in the TSS effective July 1, 2007. However, there was no easy way to visualize this type of time series data. We have created a software package called noaadata-py to process the AIS ship reports and produce KML files for viewing in Google Earth. Ship tracks can be shown changing over time to allow the viewer to feel the motion of traffic through the sanctuary. The ship tracks can also be gridded to create ship traffic density reports for specified periods of time. The density is displayed as map draped on the sea surface or as vertical histogram columns. Additional visualizations such as bathymetry images, S57 nautical charts, and USCG Marine Information for Safety and Law Enforcement (MISLE) can be combined with the ship traffic visualizations to give a more complete picture of the maritime environment. AIS traffic analyses have the potential to give managers throughout NOAA's National Marine Sanctuaries an improved ability to assess the impacts of ship traffic on the marine resources they seek to protect. Viewing ship traffic data through Google Earth provides ease and efficiency for people not trained in GIS data processing.

Assessing the environmental characteristics of cycling routes to school: a study on the reliability and validity of a Google Street View-based audit.

PubMed

Vanwolleghem, Griet; Van Dyck, Delfien; Ducheyne, Fabian; De Bourdeaudhuij, Ilse; Cardon, Greet

2014-06-10

Google Street View provides a valuable and efficient alternative to observe the physical environment compared to on-site fieldwork. However, studies on the use, reliability and validity of Google Street View in a cycling-to-school context are lacking. We aimed to study the intra-, inter-rater reliability and criterion validity of EGA-Cycling (Environmental Google Street View Based Audit - Cycling to school), a newly developed audit using Google Street View to assess the physical environment along cycling routes to school. Parents (n = 52) of 11-to-12-year old Flemish children, who mostly cycled to school, completed a questionnaire and identified their child's cycling route to school on a street map. Fifty cycling routes of 11-to-12-year olds were identified and physical environmental characteristics along the identified routes were rated with EGA-Cycling (5 subscales; 37 items), based on Google Street View. To assess reliability, two researchers performed the audit. Criterion validity of the audit was examined by comparing the ratings based on Google Street View with ratings through on-site assessments. Intra-rater reliability was high (kappa range 0.47-1.00). Large variations in the inter-rater reliability (kappa range -0.03-1.00) and criterion validity scores (kappa range -0.06-1.00) were reported, with acceptable inter-rater reliability values for 43% of all items and acceptable criterion validity for 54% of all items. EGA-Cycling can be used to assess physical environmental characteristics along cycling routes to school. However, to assess the micro-environment specifically related to cycling, on-site assessments have to be added.

The Effects of Collaborative Writing Activity Using Google Docs on Students' Writing Abilities

ERIC Educational Resources Information Center

Suwantarathip, Ornprapat; Wichadee, Saovapa

2014-01-01

Google Docs, a free web-based version of Microsoft Word, offers collaborative features which can be used to facilitate collaborative writing in a foreign language classroom. The current study compared writing abilities of students who collaborated on writing assignments using Google Docs with those working in groups in a face-to-face classroom.…

GeoSearch: a new virtual globe application for the submission, storage, and sharing of point-based ecological data

NASA Astrophysics Data System (ADS)

Cardille, J. A.; Gonzales, R.; Parrott, L.; Bai, J.

2009-12-01

How should researchers store and share data? For most of history, scientists with results and data to share have been mostly limited to books and journal articles. In recent decades, the advent of personal computers and shared data formats has made it feasible, though often cumbersome, to transfer data between individuals or among small groups. Meanwhile, the use of automatic samplers, simulation models, and other data-production techniques has increased greatly. The result is that there is more and more data to store, and a greater expectation that they will be available at the click of a button. In 10 or 20 years, will we still send emails to each other to learn about what data exist? The development and widespread familiarity with virtual globes like Google Earth and NASA WorldWind has created the potential, in just the last few years, to revolutionize the way we share data, search for and search through data, and understand the relationship between individual projects in research networks, where sharing and dissemination of knowledge is encouraged. For the last two years, we have been building the GeoSearch application, a cutting-edge online resource for the storage, sharing, search, and retrieval of data produced by research networks. Linking NASA’s WorldWind globe platform, the data browsing toolkit prefuse, and SQL databases, GeoSearch’s version 1.0 enables flexible searches and novel geovisualizations of large amounts of related scientific data. These data may be submitted to the database by individual researchers and processed by GeoSearch’s data parser. Ultimately, data from research groups gathered in a research network would be shared among users via the platform. Access is not limited to the scientists themselves; administrators can determine which data can be presented publicly and which require group membership. Under the auspices of the Canada’s Sustainable Forestry Management Network of Excellence, we have created a moderate-sized database of ecological measurements in forests; we expect to extend the approach to a Quebec lake research network encompassing decades of lake measurements. In this session, we will describe and present four related components of the new system: GeoSearch’s globe-based searching and display of scientific data; prefuse-based visualization of social connections among members of a scientific research network; geolocation of research projects using Google Spreadsheets, KML, and Google Earth/Maps; and collaborative construction of a geolocated database of research articles. Each component is designed to have applications for scientists themselves as well as the general public. Although each implementation is in its infancy, we believe they could be useful to other researcher networks.

Artificial Intelligence and NASA Data Used to Discover Eighth Planet Circling Distant Star

NASA Image and Video Library

2017-12-12

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA’s Kepler space telescope. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found by researchers from Google and The University of Texas at Austin using machine learning. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets. Video Credit: NASA Ames Research Center / Google

Accurate estimation of influenza epidemics using Google search data via ARGO.

PubMed

Yang, Shihao; Santillana, Mauricio; Kou, S C

2015-11-24

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine

PubMed Central

Dong, Jinwei; Xiao, Xiangming; Menarguez, Michael A.; Zhang, Geli; Qin, Yuanwei; Thau, David; Biradar, Chandrashekhar; Moore, Berrien

2016-01-01

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control. PMID:28025586

Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine.

PubMed

Dong, Jinwei; Xiao, Xiangming; Menarguez, Michael A; Zhang, Geli; Qin, Yuanwei; Thau, David; Biradar, Chandrashekhar; Moore, Berrien

2016-11-01

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.

Sub-pixel Area Calculation Methods for Estimating Irrigated Areas.

PubMed

Thenkabailc, Prasad S; Biradar, Chandrashekar M; Noojipady, Praveen; Cai, Xueliang; Dheeravath, Venkateswarlu; Li, Yuanjie; Velpuri, Manohar; Gumma, Muralikrishna; Pandey, Suraj

2007-10-31

The goal of this paper was to develop and demonstrate practical methods forcomputing sub-pixel areas (SPAs) from coarse-resolution satellite sensor data. Themethods were tested and verified using: (a) global irrigated area map (GIAM) at 10-kmresolution based, primarily, on AVHRR data, and (b) irrigated area map for India at 500-mbased, primarily, on MODIS data. The sub-pixel irrigated areas (SPIAs) from coarse-resolution satellite sensor data were estimated by multiplying the full pixel irrigated areas(FPIAs) with irrigated area fractions (IAFs). Three methods were presented for IAFcomputation: (a) Google Earth Estimate (IAF-GEE); (b) High resolution imagery (IAF-HRI); and (c) Sub-pixel de-composition technique (IAF-SPDT). The IAF-GEE involvedthe use of "zoom-in-views" of sub-meter to 4-meter very high resolution imagery (VHRI)from Google Earth and helped determine total area available for irrigation (TAAI) or netirrigated areas that does not consider intensity or seasonality of irrigation. The IAF-HRI isa well known method that uses finer-resolution data to determine SPAs of the coarser-resolution imagery. The IAF-SPDT is a unique and innovative method wherein SPAs aredetermined based on the precise location of every pixel of a class in 2-dimensionalbrightness-greenness-wetness (BGW) feature-space plot of red band versus near-infraredband spectral reflectivity. The SPIAs computed using IAF-SPDT for the GIAM was within2 % of the SPIA computed using well known IAF-HRI. Further the fractions from the 2 methods were significantly correlated. The IAF-HRI and IAF-SPDT help to determine annualized or gross irrigated areas (AIA) that does consider intensity or seasonality (e.g., sum of areas from season 1, season 2, and continuous year-round crops). The national census based irrigated areas for the top 40 irrigated nations (which covers about 90% of global irrigation) was significantly better related (and had lesser uncertainties and errors) when compared to SPIAs than FPIAs derived using 10-km and 500-m data. The SPIAs were closer to actual areas whereas FPIAs grossly over-estimate areas. The research clearly demonstrated the value and the importance of sub-pixel areas as opposed to full pixel areas and presented 3 innovative methods for computing the same.

An Object-Based Machine Learning Classification Procedure for Mapping Impoundments in Brazil's Amazon-Cerrado Agricultural Frontier

NASA Astrophysics Data System (ADS)

Solvik, K.; Macedo, M.; Graesser, J.; Lathuilliere, M. J.

2017-12-01

Large-scale agriculture and cattle ranching in Brazil has driving the creation of tens of thousands of small stream impoundments to provide water for crops and livestock. These impoundments are a source of methane emissions and have significant impacts on stream temperature, connectivity, and water use over a large region. Due to their large numbers and small size, they are difficult to map using conventional methods. Here, we present a two-stage object-based supervised classification methodology for identifying man-made impoundments in Brazil. First, in Google Earth Engine pixels are classified as water or non-water using satellite data and HydroSHEDS products as predictors. Second, using Python's scikit-learn and scikit-image modules the water objects are classified as man-made or natural based on a variety of shape and spectral properties. Both classifications are performed by a random forest classifier. Training data is acquired by visually identifying impoundments and natural water bodies using high resolution satellite imagery from Google Earth.This methodology was applied to the state of Mato Grosso using a cloud-free mosaic of Sentinel 1 (10m resolution) radar and Sentinel 2 (10-20m) multispectral data acquired during the 2016 dry season. Independent test accuracy was estimated at 95% for the first stage and 93% for the second. We identified 54,294 man-made impoundments in Mato Grosso in 2016. The methodology is generalizable to other high resolution satellite data and has been tested on Landsat 5 and 8 imagery. Applying the same approach to Landsat 8 images (30 m), we identified 35,707 impoundments in the 2015 dry season. The difference in number is likely because the coarser-scale imagery fails to detect small (< 900 m2) objects. On-going work will apply this approach to satellite time series for the entire Amazon-Cerrado frontier, allowing us to track changes in the number, size, and distribution of man-made impoundments. Automated impoundment mapping over large areas may help with management of streams in agricultural landscapes in Brazil and other tropical regions.

Comparison of PubMed and Google Scholar literature searches.

PubMed

Anders, Michael E; Evans, Dennis P

2010-05-01

Literature searches are essential to evidence-based respiratory care. To conduct literature searches, respiratory therapists rely on search engines to retrieve information, but there is a dearth of literature on the comparative efficiencies of search engines for researching clinical questions in respiratory care. To compare PubMed and Google Scholar search results for clinical topics in respiratory care to that of a benchmark. We performed literature searches with PubMed and Google Scholar, on 3 clinical topics. In PubMed we used the Clinical Queries search filter. In Google Scholar we used the search filters in the Advanced Scholar Search option. We used the reference list of a related Cochrane Collaboration evidence-based systematic review as the benchmark for each of the search results. We calculated recall (sensitivity) and precision (positive predictive value) with 2 x 2 contingency tables. We compared the results with the chi-square test of independence and Fisher's exact test. PubMed and Google Scholar had similar recall for both overall search results (71% vs 69%) and full-text results (43% vs 51%). PubMed had better precision than Google Scholar for both overall search results (13% vs 0.07%, P < .001) and full-text results (8% vs 0.05%, P < .001). Our results suggest that PubMed searches with the Clinical Queries filter are more precise than with the Advanced Scholar Search in Google Scholar for respiratory care topics. PubMed appears to be more practical to conduct efficient, valid searches for informing evidence-based patient-care protocols, for guiding the care of individual patients, and for educational purposes.

Identifying Severe Weather Impacts and Damage with Google Earth Engine

NASA Astrophysics Data System (ADS)

Molthan, A.; Burks, J. E.; Bell, J. R.

2015-12-01

Hazards associated with severe convective storms can lead to rapid changes in land surface vegetation. Depending upon the type of vegetation that has been impacted, their impacts can be relatively short lived, such as damage to seasonal crops that are eventually removed by harvest, or longer-lived, such as damage to a stand of trees or expanse of forest that require several years to recover. Since many remote sensing imagers provide their highest spatial resolution bands in the red and near-infrared to support monitoring of vegetation, these impacts can be readily identified as short-term and marked decreases in common vegetation indices such as NDVI, along with increases in land surface temperature that are observed at a reduced spatial resolution. The ability to identify an area of vegetation change is improved by understanding the conditions that are normal for a given time of year and location, along with a typical range of variability in a given parameter. This analysis requires a period of record well beyond the availability of near real-time data. These activities would typically require an analyst to download large volumes of data from sensors such as NASA's MODIS (aboard Terra and Aqua) or higher resolution imagers from the Landsat series of satellites. Google's Earth Engine offers a "big data" solution to these challenges, by providing a streamlined API and option to process the period of record of NASA MODIS and Landsat products through relatively simple Javascript coding. This presentation will highlight efforts to date in using Earth Engine holdings to produce vegetation and land surface temperature anomalies that are associated with damage to agricultural and other vegetation caused by severe thunderstorms across the Central and Southeastern United States. Earth Engine applications will show how large data holdings can be used to map severe weather damage, ascertain longer-term impacts, and share best practices learned and challenges with applying Earth Engine holdings to the analysis of severe weather damage. Other applications are also demonstrated, such as use of Earth Engine to prepare pre-event composites that can be used to subjectively identify other severe weather impacts. Future extension to flooding and wildfires is also proposed.

Using Google Documents for Composing Projects that Use Primary Research in First-Year Writing Courses

ERIC Educational Resources Information Center

Strasma, Kip

2010-01-01

In this article, the author shares how efficient and effective Google Documents is for faculty seeking to engage students in inquiry-based, emergent, and primary research in first-year composition courses. The specific appeal of Google Documents is that it occupies a space between "open source"--defined by the Open Source Initiative as "free,…

Google searches help with diagnosis in dermatology.

PubMed

Amri, Montassar; Feroz, Kaliyadan

2014-01-01

Several previous studies have tried to assess the usefulness of Google search as a diagnostic aid. The results were discordant and have led to controversies. To investigate how often Google search is helpful to reach correct diagnoses in dermatology. Two fifth-year students (A and B) and one demonstrator (C) have participated as investigators in this paper. Twenty-five diagnostic dermatological cases were selected from all the clinical cases published in the Web only images in clinical medicine from March 2005 to November 2009. The main outcome measure of our paper was to compare the number of correct diagnoses provided by the investigators without, and with Google search. Investigator A gave correct diagnoses in 9/25 (36%) cases without Google search, his diagnostic success after Google search was 18/25 (72%). Investigator B results were 11/25 (44%) correct diagnoses without Google search, and 19/25 (76%) after this search. For investigator C, the results were 12/25 (48%) without Google search, and 18/25 (72%) after the use of this tool. Thus, the total correct diagnoses provided by the three investigators were 32 (42.6%) without Google search, and 55 (73.3%) when using this facility. The difference was statistically significant between the total number of correct diagnoses given by the three investigators without, and with Google search (p = 0.0002). In the light of our paper, Google search appears to be an interesting diagnostic aid in dermatology. However, we emphasize that diagnosis is primarily an art based on clinical skills and experience.

Croatian Medical Journal Citation Score in Web of Science, Scopus, and Google Scholar

PubMed Central

Šember, Marijan; Utrobičić, Ana; Petrak, Jelka

2010-01-01

Aim To analyze the 2007 citation count of articles published by the Croatian Medical Journal in 2005-2006 based on data from the Web of Science, Scopus, and Google Scholar. Methods Web of Science and Scopus were searched for the articles published in 2005-2006. As all articles returned by Scopus were included in Web of Science, the latter list was the sample for further analysis. Total citation counts for each article on the list were retrieved from Web of Science, Scopus, and Google Scholar. The overlap and unique citations were compared and analyzed. Proportions were compared using χ2-test. Results Google Scholar returned the greatest proportion of articles with citations (45%), followed by Scopus (42%), and Web of Science (38%). Almost a half (49%) of articles had no citations and 11% had an equal number of identical citations in all 3 databases. The greatest overlap was found between Web of Science and Scopus (54%), followed by Scopus and Google Scholar (51%), and Web of Science and Google Scholar (44%). The greatest number of unique citations was found by Google Scholar (n = 86). The majority of these citations (64%) came from journals, followed by books and PhD theses. Approximately 55% of all citing documents were full-text resources in open access. The language of citing documents was mostly English, but as many as 25 citing documents (29%) were in Chinese. Conclusion Google Scholar shares a total of 42% citations returned by two others, more influential, bibliographic resources. The list of unique citations in Google Scholar is predominantly journal based, but these journals are mainly of local character. Citations received by internationally recognized medical journals are crucial for increasing the visibility of small medical journals but Google Scholar may serve as an alternative bibliometric tool for an orientational citation insight. PMID:20401951

Modern Publishing Approach of Journal of Astronomy & Earth Sciences Education

NASA Astrophysics Data System (ADS)

Slater, Timothy F.

2015-01-01

Filling a needed scholarly publishing avenue for astronomy education researchers and earth science education researchers, the Journal of Astronomy & Earth Sciences Education - JAESE published its first volume and issue in 2014. The Journal of Astronomy & Earth Sciences Education - JAESE is a scholarly, peer-reviewed scientific journal publishing original discipline-based education research and evaluation, with an emphasis of significant scientific results derived from ethical observations and systematic experimentation in science education and evaluation. International in scope, JAESE aims to publish the highest quality and timely articles from discipline-based education research that advance understanding of astronomy and earth sciences education and are likely to have a significant impact on the discipline or on policy. Articles are solicited describing both (i) systematic science education research and (ii) evaluated teaching innovations across the broadly defined Earth & space sciences education, including the disciplines of astronomy, climate education, energy resource science, environmental science, geology, geography, agriculture, meteorology, planetary sciences, and oceanography education. The publishing model adopted for this new journal is open-access and articles appear online in GoogleScholar, ERIC, and are searchable in catalogs of 440,000 libraries that index online journals of its type. Rather than paid for by library subscriptions or by society membership dues, the annual budget is covered by page-charges paid by individual authors, their institutions, grants or donors: This approach is common in scientific journals, but is relatively uncommon in education journals. Authors retain their own copyright. The journal is owned by the Clute Institute of Denver, which owns and operates 17 scholarly journals and currently edited by former American Astronomical Society Education Officer Tim Slater, who is an endowed professor at the University of Wyoming and a Senior Scientist at the CAPER Center for Astronomy & Physics Education Research. More information about the journal and its policies are available online at http://www.JAESE.org

Object recognition based on Google's reverse image search and image similarity

NASA Astrophysics Data System (ADS)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

Reaching Forward in the War against the Islamic State

DTIC Science & Technology

2016-12-07

every week with U.S.- and Coalition-advised ISOF troops taking the lead in combat operations using cellular communications systems that link them...tions—Offline Maps, Google Earth , and Viber, to name a few—which allowed them to bring tablets and phones on their operations to help communicate ...provided an initial Remote Advise and Assist capability that enabled the special forces advisors to track, communicate , and share limited data with

Baseline coastal oblique aerial photographs collected from Navarre Beach, Florida, to Breton Island, Louisiana, September 18–19, 2015

USGS Publications Warehouse

Morgan, Karen L. M.

2016-08-01

In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then the thumbnail or the link below the thumbnail. The KML file was created using the photographic navigation files. This KML file can be found in the kml folder.

Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth

USGS Publications Warehouse

Sato, H.P.; Harp, E.L.

2009-01-01

The 12 May 2008 M7.9 Wenchuan earthquake in the People's Republic of China represented a unique opportunity for the international community to use commonly available GIS (Geographic Information System) tools, like Google Earth (GE), to rapidly evaluate and assess landslide hazards triggered by the destructive earthquake and its aftershocks. In order to map earthquake-triggered landslides, we provide details on the applicability and limitations of publicly available 3-day-post- and pre-earthquake imagery provided by GE from the FORMOSAT-2 (formerly ROCSAT-2; Republic of China Satellite 2). We interpreted landslides on the 8-m-resolution FORMOSAT-2 image by GE; as a result, 257 large landslides were mapped with the highest concentration along the Beichuan fault. An estimated density of 0.3 landslides/km2 represents a minimum bound on density given the resolution of available imagery; higher resolution data would have identified more landslides. This is a preliminary study, and further study is needed to understand the landslide characteristics in detail. Although it is best to obtain landslide locations and measurements from satellite imagery having high resolution, it was found that GE is an effective and rapid reconnaissance tool. ?? 2009 Springer-Verlag.

An Interactive Visual Analytics Framework for Multi-Field Data in a Geo-Spatial Context

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Zhiyuan; Tong, Xiaonan; McDonnell, Kevin T.

2013-04-01

Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multi field visualization problem, where the geospace provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivari ate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementationmore » that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixedwindow brushing and correlationenhanced display. We conceived our system with a team of climate researchers, who already made a few important discov eries using it. This demonstrates our system’s great potential to enable scientific discoveries, possibly also in oth er domains where data have a geospatial reference.« less

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

PubMed Central

Hopkins, Richard S; Cook, Robert L; Striley, Catherine W

2016-01-01

Background Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC’s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package “bcp” version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Conclusions Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. PMID:27765731

Using Project-Based Learning and Google Docs to Support Diversity

ERIC Educational Resources Information Center

Leh, Amy

2014-01-01

A graduate course, ETEC543 ("Technology and Learning I"), was revised to better serve increasing new student population, international students, in an academic program. Project-based learning, Google Docs, and instructional strategies fostering diversity and critical thinking were incorporated into the course redesign. Observations,…

Accurate estimation of influenza epidemics using Google search data via ARGO

PubMed Central

Yang, Shihao; Santillana, Mauricio; Kou, S. C.

2015-01-01

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search–based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people’s online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions. PMID:26553980

Earth Matters: Promoting Science Exploration through Blogs and Social Media

NASA Astrophysics Data System (ADS)

Ward, K.; Voiland, A. P.; Carlowicz, M. J.; Simmon, R. B.; Allen, J.; Scott, M.; Przyborski, P. D.

2012-12-01

NASA's Earth Observatory (EO) is a 13-year old online publication focusing on the communication of NASA Earth science research, including climate change, weather, geology, oceanography, and solar flares. We serve two primary audiences: the "attentive public"--people interested in and willing to seek out information about science, technology, and the environment--and popular media. We use the EO website (earthobservatory.nasa.gov) to host a variety of content including image-driven stories (natural events and research-based), articles featuring NASA research and, more recently, blogs that give us the ability to increase interaction with our users. For much of our site's history, our communication has been largely one way, and we have relied primarily on traditional online marketing techniques such as RSS and email listservs. As the information ecosystem evolves into one in which many users expect to play a more active role in distributing and even developing content through social media, we've experimented with various social media outlets (blogs, Twitter, Facebook, Google+, etc.) that offer new opportunities for people to interact with NASA data, scientists, and the EO editorial team. As part of our explorations, we are learning about how, and to what extent, these outlets can be used for interaction and outright promotion and how to achieve those goals with existing personnel and resources.

75 FR 5310 - Combined Notice of Filings #1

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-02

... Wednesday, February 10, 2010. Docket Numbers: ER10-468-001. Applicants: Google Energy LLC. Description: Clarifying amendments to Google Energy LLC's Application for market based rate authority and granting of...

Map of Life - A Dashboard for Monitoring Planetary Species Distributions

NASA Astrophysics Data System (ADS)

Jetz, W.

2016-12-01

Geographic information about biodiversity is vital for understanding the many services nature provides and their potential changes, yet remains unreliable and often insufficient. By integrating a wide range of knowledge about species distributions and their dynamics over time, Map of Life supports global biodiversity education, monitoring, research and decision-making. Built on a scalable web platform geared for large biodiversity and environmental data, Map of Life endeavors provides species range information globally and species lists for any area. With data and technology provided by NASA and Google Earth Engine, tools under development use remote sensing-based environmental layers to enable on-the-fly predictions of species distributions, range changes, and early warning signals for threatened species. The ultimate vision is a globally connected, collaborative knowledge- and tool-base for regional and local biodiversity decision-making, education, monitoring, and projection. For currently available tools, more information and to follow progress, go to MOL.org.

INSPIRE: Initiating New Science Partnerships in Rural Education

NASA Astrophysics Data System (ADS)

Pierce, Donna M.; McNeal, K. S.; Bruce, L. M.; Harpole, S. H.; Schmitz, D. W.

2010-10-01

INSPIRE, Initiating New Science Partnerships in Rural Education, is a partnership between Mississippi State University and three school districts in Mississippi's Golden Triangle (Starkville, Columbus, West Point). This program recruits ten graduate fellows each year from geosciences, physics, astronomy, and engineering and pairs them with a participating middle school or high school teacher. The graduate fellows provide technology-supported inquiry-based learning in the earth and space sciences by incorporating their research into classroom instruction and using multiple resources such as Google Earth, geographic information systems (GIS), Celestia, and others. In addition to strengthening the communication skills of the graduate fellows, INSPIRE will increase the content knowledge of participating teachers, provide high-quality instruction using multiple technologies, promote higher education to area high-school students, and provide fellows and teachers with international research experience through our partners in Australia, The Bahamas, England, and Poland. INSPIRE is funded by the Graduate STEM Fellows in K-12 Education Program (GK-12; Award No. DGE-0947419), which is part of the Division for Graduate Education of the National Science Foundation.

Implementing a Global Tool for Mercy Corps Based on Spatially Continuous Precipitation Analysis for Resiliency Monitoring and Measuring at the Community-Scale

NASA Astrophysics Data System (ADS)

Tomlin, J. N.; El-Behaedi, R.; McCartney, S.; Lingo, R.; Thieme, A.

2017-12-01

Global water resources are important for societies, economies, and the environment. In Niger, limited water resources restrict the expansion of agriculture and communities. Mercy Corps currently works in over 40 countries around the world to address a variety of stresses which include water resources and building long-term food resilience. As Mercy Corps seeks to integrate the use of Earth observations, NASA has established a partnership to help facilitate this effort incorporating Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data to create a standardized precipitation index that highlights low and high rainfall from 1981 - 2016. The team created a Google Earth Engine tool that combines precipitation data with other metrics of stress in Niger. The system is designed to be able to incorporate groundwater storage data as it becomes available. This tool allows for near real-time updates of trends in precipitation and improves Mercy Corps' ability to spatially evaluate changes in resiliency by monitoring shocks and stressors.

FluBreaks: early epidemic detection from Google flu trends.

PubMed

Pervaiz, Fahad; Pervaiz, Mansoor; Abdur Rehman, Nabeel; Saif, Umar

2012-10-04

The Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases weeks in advance of CDC records. However, contrary to popular belief, Google Flu Trends is not an early epidemic detection system. Instead, it is designed as a baseline indicator of the trend, or changes, in the number of disease cases. To evaluate whether these trends can be used as a basis for an early warning system for epidemics. We present the first detailed algorithmic analysis of how Google Flu Trends can be used as a basis for building a fully automated system for early warning of epidemics in advance of methods used by the CDC. Based on our work, we present a novel early epidemic detection system, called FluBreaks (dritte.org/flubreaks), based on Google Flu Trends data. We compared the accuracy and practicality of three types of algorithms: normal distribution algorithms, Poisson distribution algorithms, and negative binomial distribution algorithms. We explored the relative merits of these methods, and related our findings to changes in Internet penetration and population size for the regions in Google Flu Trends providing data. Across our performance metrics of percentage true-positives (RTP), percentage false-positives (RFP), percentage overlap (OT), and percentage early alarms (EA), Poisson- and negative binomial-based algorithms performed better in all except RFP. Poisson-based algorithms had average values of 99%, 28%, 71%, and 76% for RTP, RFP, OT, and EA, respectively, whereas negative binomial-based algorithms had average values of 97.8%, 17.8%, 60%, and 55% for RTP, RFP, OT, and EA, respectively. Moreover, the EA was also affected by the region's population size. Regions with larger populations (regions 4 and 6) had higher values of EA than region 10 (which had the smallest population) for negative binomial- and Poisson-based algorithms. The difference was 12.5% and 13.5% on average in negative binomial- and Poisson-based algorithms, respectively. We present the first detailed comparative analysis of popular early epidemic detection algorithms on Google Flu Trends data. We note that realizing this opportunity requires moving beyond the cumulative sum and historical limits method-based normal distribution approaches, traditionally employed by the CDC, to negative binomial- and Poisson-based algorithms to deal with potentially noisy search query data from regions with varying population and Internet penetrations. Based on our work, we have developed FluBreaks, an early warning system for flu epidemics using Google Flu Trends.

Use of Openly Available Satellite Images for Remote Sensing Education

NASA Astrophysics Data System (ADS)

Wang, C.-K.

2011-09-01

With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remote sensing education. It is obvious that the remote sensing education should be adjusted to the fact that the audience are already the customers of remote sensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remote sensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward Remote Sensing.

75 FR 357 - Combined Notice of Filings #1

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-05

..., January 13, 2010. Docket Numbers: ER10-468-000. Applicants: Google Energy LLC. Description: Application of Google Energy LLC for Market Based Rate Authority and Granting of Waivers and Blanket Authority. Filed...

A Mixed Methods Analysis of the Effect of Google Docs Environment on EFL Learners' Writing Performance and Causal Attributions for Success and Failure

ERIC Educational Resources Information Center

Seyyedrezaie, Zari Sadat; Ghonsooly, Behzad; Shahriari, Hesamoddin; Fatemi, Hazar Hosseini

2016-01-01

This study investigated the effect of writing process in Google Docs environment on Iranian EFL learners' writing performance. It also examined students' perceptions towards the effects of Google Docs and their perceived causes of success or failure in writing performance. In this regard, 48 EFL students were chosen based on their IELTs writing…

Data Access and Web Services at the EarthScope Plate Boundary Observatory

NASA Astrophysics Data System (ADS)

Matykiewicz, J.; Anderson, G.; Henderson, D.; Hodgkinson, K.; Hoyt, B.; Lee, E.; Persson, E.; Torrez, D.; Smith, J.; Wright, J.; Jackson, M.

2007-12-01

The EarthScope Plate Boundary Observatory (PBO) at UNAVCO, Inc., part of the NSF-funded EarthScope project, is designed to study the three-dimensional strain field resulting from deformation across the active boundary zone between the Pacific and North American plates in the western United States. To meet these goals, PBO will install 880 continuous GPS stations, 103 borehole strainmeter stations, and five laser strainmeters, as well as manage data for 209 previously existing continuous GPS stations and one previously existing laser strainmeter. UNAVCO provides access to data products from these stations, as well as general information about the PBO project, via the PBO web site (http://pboweb.unavco.org). GPS and strainmeter data products can be found using a variety of access methods, incuding map searches, text searches, and station specific data retrieval. In addition, the PBO construction status is available via multiple mapping interfaces, including custom web based map widgets and Google Earth. Additional construction details can be accessed from PBO operational pages and station specific home pages. The current state of health for the PBO network is available with the statistical snap-shot, full map interfaces, tabular web based reports, and automatic data mining and alerts. UNAVCO is currently working to enhance the community access to this information by developing a web service framework for the discovery of data products, interfacing with operational engineers, and exposing data services to third party participants. In addition, UNAVCO, through the PBO project, provides advanced data management and monitoring systems for use by the community in operating geodetic networks in the United States and beyond. We will demonstrate these systems during the AGU meeting, and we welcome inquiries from the community at any time.

Towards the Geospatial Web: Media Platforms for Managing Geotagged Knowledge Repositories

NASA Astrophysics Data System (ADS)

Scharl, Arno

International media have recognized the visual appeal of geo-browsers such as NASA World Wind and Google Earth, for example, when Web and television coverage on Hurricane Katrina used interactive geospatial projections to illustrate its path and the scale of destruction in August 2005. Yet these early applications only hint at the true potential of geospatial technology to build and maintain virtual communities and to revolutionize the production, distribution and consumption of media products. This chapter investigates this potential by reviewing the literature and discussing the integration of geospatial and semantic reference systems, with an emphasis on extracting geospatial context from unstructured text. A content analysis of news coverage based on a suite of text mining tools (webLyzard) sheds light on the popularity and adoption of geospatial platforms.

Building Extraction Based on Openstreetmap Tags and Very High Spatial Resolution Image in Urban Area

NASA Astrophysics Data System (ADS)

Kang, L.; Wang, Q.; Yan, H. W.

2018-04-01

How to derive contour of buildings from VHR images is the essential problem for automatic building extraction in urban area. To solve this problem, OSM data is introduced to offer vector contour information of buildings which is hard to get from VHR images. First, we import OSM data into database. The line string data of OSM with tags of building, amenity, office etc. are selected and combined into completed contours; Second, the accuracy of contours of buildings is confirmed by comparing with the real buildings in Google Earth; Third, maximum likelihood classification is conducted with the confirmed building contours, and the result demonstrates that the proposed approach is effective and accurate. The approach offers a new way for automatic interpretation of VHR images.

Baseline coastal oblique aerial photographs collected from Key Largo, Florida, to the Florida/Georgia border, September 5-6, 2014

USGS Publications Warehouse

Morgan, Karen L. M.

2015-09-14

In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files. These KML files can be found in the kml folder.

Isosurface Display of 3-D Scalar Fields from a Meteorological Model on Google Earth

DTIC Science & Technology

2013-07-01

facets to four, we have chosen to adopt and implement a revised method discussed and made available by Bourke (1994), which can accommodate up to...five facets for a given grid cube. While the published code from Bourke (1994) is in the public domain, it was originally implemented in the C...and atmospheric temperatures. 17 4. References Bourke , P. Polygonising a Scalar Field. http://paulbourke.net/geometry/polygonise

Effects of Spatial Ability, Gender Differences, and Pictorial Training on Children Using 2-D and 3-D Environments to Recall Landmark Locations from Memory

ERIC Educational Resources Information Center

Kopcha, Theodore J.; Otumfuor, Beryl A.; Wang, Lu

2015-01-01

This study examines the effects of spatial ability, gender differences, and pictorial training on fourth grade students' ability to recall landmark locations from memory. Ninety-six students used Google Earth over a 3-week period to locate landmarks (3-D) and mark their location on a 2-D topographical map. Analysis of covariance on posttest scores…

Too Cool for School? No Way! Using the TPACK Framework: You Can Have Your Hot Tools and Teach with Them, Too

ERIC Educational Resources Information Center

Mishra, Punya; Koehler, Matthew

2009-01-01

This is the age of cool tools. Facebook, iPhone, Flickr, blogs, cloud computing, Smart Boards, YouTube, Google Earth, and GPS are just a few examples of new technologies that bombard people from all directions. As individuals people see a new technology and can appreciate its coolness, but as educators they wonder how these tools can be used for…

Influenza forecasting with Google Flu Trends.

PubMed

Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

2013-01-01

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

NASA Technical Reports Server (NTRS)

Liu, Z.; Ostrenga, D.; Vollmer, B.; Kempler, S.; Deshong, B.; Greene, M.

2015-01-01

The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is also home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 17 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available: -Level-1 GPM Microwave Imager (GMI) and partner radiometer products, DPR products -Level-2 Goddard Profiling Algorithm (GPROF) GMI and partner products, DPR products -Level-3 daily and monthly products, DPR products -Integrated Multi-satellitE Retrievals for GPM (IMERG) products (early, late, and final) A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http://disc.sci.gsfc.nasa.gov/gpm). Data services that are currently and to-be available include Google-like Mirador (http://mirador.gsfc.nasa.gov/) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http://giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications. The United User Interface (UUI) is the next step in the evolution of the GES DISC web site. It attempts to provide seamless access to data, information and services through a single interface without sending the user to different applications or URLs (e.g., search, access, subset, Giovanni, documents).

Landforms in Lidar: Building a Catalog of Digital Landforms for Education and Outreach

NASA Astrophysics Data System (ADS)

Kleber, E.; Crosby, C.; Olds, S. E.; Arrowsmith, R.

2012-12-01

Lidar (Light Detection and Ranging) has emerged as a fundamental tool in the earth sciences. The collection of high-resolution lidar topography from an airborne or terrestrial platform allows landscapes and landforms to be spatially represented in at sub-meter resolution and in three dimensions. While the growing availability of lidar has led to numerous new scientific findings, these data also have tremendous value for earth science education. The study of landforms is an essential and basic element of earth science education that helps students to grasp fundamental earth system processes and how they manifest themselves in the world around us. Historically students are introduced to landforms and related processes through diagrams and images seen in earth science textbooks. Lidar data, coupled with free tools such as Google Earth, provide a means to allow students and the interested public to visualize, explore, and interrogate these same landforms in an interactive manner not possible in two-dimensional remotely sensed imagery. The NSF-funded OpenTopography facility hosts data collected for geologic, hydrologic, and biological research, covering a diverse range of landscapes, and thus provides a wealth of data that could be incorporated into educational materials. OpenTopography, in collaboration with UNAVCO, are developing a catalog of classic geologic landforms depicted in lidar. Beginning with textbook-examples of features such as faults and tectonic landforms, dunes, fluvial and glacial geomorphology, and natural hazards such as landslides and volcanoes, the catalog will be an online resource for educators and the interested public. Initially, the landforms will be sourced from pre-existing datasets hosted by OpenTopography. Users will see an image representative of the landform then have the option to download the data in Google Earth KMZ format, as a digital elevation model, or the original lidar point cloud file. By providing the landform in a range of data types, educators can choose to load the image into a presentation, work with the data in a GIS, or do more advanced data analysis on the original point cloud data. In addition, for each landform, links to additional online resources and a bibliography of select publications will be provided. OpenTopography will initially seed the lidar landform catalog, but ultimately the goal is to solicit community contributions as well. We envision the catalog development as the first phase of this activity, and hope that later activities will focus on building curriculum that leverages the catalog and lidar data to teach earth system processes.

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

NASA Technical Reports Server (NTRS)

Xiong, Jun; Thenkabail, Prasad S.; Tilton, James C.; Gumma, Murali K.; Teluguntla, Pardhasaradhi; Oliphant, Adam; Congalton, Russell G.; Yadav, Kamini; Gorelick, Noel

2017-01-01

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Illuminating Northern California’s Active Faults

USGS Publications Warehouse

Prentice, Carol S.; Crosby, Christopher J.; Whitehill, Caroline S.; Arrowsmith, J. Ramon; Furlong, Kevin P.; Philips, David A.

2009-01-01

Newly acquired light detection and ranging (lidar) topographic data provide a powerful community resource for the study of landforms associated with the plate boundary faults of northern California (Figure 1). In the spring of 2007, GeoEarthScope, a component of the EarthScope Facility construction project funded by the U.S. National Science Foundation, acquired approximately 2000 square kilometers of airborne lidar topographic data along major active fault zones of northern California. These data are now freely available in point cloud (x, y, z coordinate data for every laser return), digital elevation model (DEM), and KMZ (zipped Keyhole Markup Language, for use in Google EarthTM and other similar software) formats through the GEON OpenTopography Portal (http://www.OpenTopography.org/data). Importantly, vegetation can be digitally removed from lidar data, producing high-resolution images (0.5- or 1.0-meter DEMs) of the ground surface beneath forested regions that reveal landforms typically obscured by vegetation canopy (Figure 2)

Using NASA's Giovanni Web Portal to Access and Visualize Satellite-Based Earth Science Data in the Classroom

NASA Astrophysics Data System (ADS)

Lloyd, S. A.; Acker, J. G.; Prados, A. I.; Leptoukh, G. G.

2008-12-01

One of the biggest obstacles for the average Earth science student today is locating and obtaining satellite- based remote sensing datasets in a format that is accessible and optimal for their data analysis needs. At the Goddard Earth Sciences Data and Information Services Center (GES-DISC) alone, on the order of hundreds of Terabytes of data are available for distribution to scientists, students and the general public. The single biggest and time-consuming hurdle for most students when they begin their study of the various datasets is how to slog through this mountain of data to arrive at a properly sub-setted and manageable dataset to answer their science question(s). The GES DISC provides a number of tools for data access and visualization, including the Google-like Mirador search engine and the powerful GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) web interface. Giovanni provides a simple way to visualize, analyze and access vast amounts of satellite-based Earth science data. Giovanni's features and practical examples of its use will be demonstrated, with an emphasis on how satellite remote sensing can help students understand recent events in the atmosphere and biosphere. Giovanni is actually a series of sixteen similar web-based data interfaces, each of which covers a single satellite dataset (such as TRMM, TOMS, OMI, AIRS, MLS, HALOE, etc.) or a group of related datasets (such as MODIS and MISR for aerosols, SeaWIFS and MODIS for ocean color, and the suite of A-Train observations co-located along the CloudSat orbital path). Recently, ground-based datasets have been included in Giovanni, including the Northern Eurasian Earth Science Partnership Initiative (NEESPI), and EPA fine particulate matter (PM2.5) for air quality. Model data such as the Goddard GOCART model and MERRA meteorological reanalyses (in process) are being increasingly incorporated into Giovanni to facilitate model- data intercomparison. A full suite of data analysis and visualization tools is also available within Giovanni. The GES DISC is currently developing a systematic series of training modules for Earth science satellite data, associated with our development of additional datasets and data visualization tools for Giovanni. Training sessions will include an overview of the Earth science datasets archived at Goddard, an overview of terms and techniques associated with satellite remote sensing, dataset-specific issues, an overview of Giovanni functionality, and a series of examples of how data can be readily accessed and visualized.

The Future of Risk Analysis: Operationalizing Living Vulnerability Assessments from the Cloud to the Street (and Back)

NASA Astrophysics Data System (ADS)

Tellman, B.; Schwarz, B.; Kuhn, C.; Pandey, B.; Schank, C.; Sullivan, J.; Mahtta, R.; Hammet, L.

2016-12-01

21 million people are exposed to flooding every year, and that number is expected to more than double by 2030 due to climate, land use, and demographic change. Cloud to Street, a mission driven science organization, is working to make big and real time data more meaningful to understand both biophysical and social vulnerability to flooding in this changing world. This talk will showcase the science and practice we have built of integrated social and biophysical flood vulnerability assessments based on our work in Uttarakhand, India and Senegal, in conjunction with nonprofits and development banks. We will show developments of our global historical flood database, detected from MODIS and Landsat satellites, used to power machine learning flood exposure models in Google Earth Engine's API. Demonstrating the approach, we will also showcase new approaches in social vulnerability science, from developing data-driven social vulnerability indices in India, to deriving predictive models that explain the social conditions that lead to disproportionate flood damage and fatality in the US. While this talk will draw on examples of completed vulnerability assessments, we will also discuss the possible future for place-based "living" flood vulnerability assessments that are updated each time satellites circle the earth or people add crowd-sourced observations about flood events and social conditions.

Using Internet-Based Applications to Increase Collaboration among Stakeholders in Special Education

ERIC Educational Resources Information Center

Riggleman, Samantha; Buchter, Jennifer M.

2017-01-01

Input from parents of children with disabilities is highly valuable; however, barriers such as lack of time or travel can hinder the partnership between parents and stakeholders. Internet-based applications (e.g., Google Groups, Google Drive, and OneDrive) can alleviate some of the difficulties in collaborating. This article describes the use and…

Spatial Distribution of the Population at Risk of Cholangiocarcinoma in Chum Phaung District, Nakhon Ratchasima Province of Thailand.

PubMed

Kaewpitoon, Soraya J; Rujirakul, Ratana; Loyd, Ryan A; Matrakool, Likit; Sangkudloa, Amnat; Kaewthani, Sarochinee; Khemplila, Kritsakorn; Eaksanti, Thawatchai; Phatisena, Tanida; Kujapun, Jirawoot; Norkaew, Jun; Joosiri, Apinya; Kaewpitoon, Natthawut

2016-01-01

Cholangiocarcinoma (CCA) is a serious health problem in Thailand, particularly in northeastern and northern regions, but epidemiological studies are scarce and the spatial distribution of CCA remains to be determined. A database for the population at risk is required for monitoring, surveillance and organization of home health care. This study aim was to geo-visually display the distribution of CCA in northeast Thailand, using a geographic information system and Google Earth. A cross-sectional survey was carried out in 9 sub-districts and 133 villages in Chum Phuang district, Nakhon Ratchasima province during June and October 2015. Data on demography, and the population at risk for CCA were combined with the points of villages, sub-district boundaries, district boundaries, and points of hospitals in districts, then fed into a geographical information system. After the conversion, all of the data were imported into Google Earth for geo-visualization. A total of 11,960 from 83,096 population were included in this study. Females and male were 52.5%, and 47.8%, the age group 41-50 years old 33.3%. Individual risk for CCA was identifed and classified by using the Korat CCA verbal screening test as low (92.8%), followed by high risk (6.74%), and no (0.49%), respectively. Gender (X2-test=1143.63, p-value= 0.001), age group (X2-test==211.36, p-value=0.0001), and sub-district (X2-test=1471.858, p-value=0.0001) were significantly associated with CCA risk. Spatial distribution of the population at risk for CCA in Chum Phuang district was viewed with Google Earth. Geo-visual display followed Layer 1: District, Layer 2: Sub-district, Layer 3: Number of low risk in village, Layer 4: Number of high risk in village, and Layer 5: Hospital in Chum Phuang District and their related catchment areas. We present the first risk geo-visual display of CCA in this rural community, which is important for spatial targeting of control efforts. Risk appears to be strongly associated with gender, age group, and sub-district. Therefor, spatial distribution is suitable for the use in the further monitoring, surveillance, and home health care for CCA.

Immunochromatographic diagnostic test analysis using Google Glass.

PubMed

Feng, Steve; Caire, Romain; Cortazar, Bingen; Turan, Mehmet; Wong, Andrew; Ozcan, Aydogan

2014-03-25

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.e., the user) and also stored on a central server along with the RDT image, QR code, and other related information (e.g., demographic data). The same server also provides a dynamic spatiotemporal map and real-time statistics for uploaded RDT results accessible through Internet browsers. We tested this Google Glass-based diagnostic platform using qualitative (i.e., yes/no) human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) tests. For the quantitative RDTs, we measured activated tests at various concentrations ranging from 0 to 200 ng/mL for free and total PSA. This wearable RDT reader platform running on Google Glass combines a hands-free sensing and image capture interface with powerful servers running our custom image processing codes, and it can be quite useful for real-time spatiotemporal tracking of various diseases and personal medical conditions, providing a valuable tool for epidemiology and mobile health.

Immunochromatographic Diagnostic Test Analysis Using Google Glass

PubMed Central

2014-01-01

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.e., the user) and also stored on a central server along with the RDT image, QR code, and other related information (e.g., demographic data). The same server also provides a dynamic spatiotemporal map and real-time statistics for uploaded RDT results accessible through Internet browsers. We tested this Google Glass-based diagnostic platform using qualitative (i.e., yes/no) human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) tests. For the quantitative RDTs, we measured activated tests at various concentrations ranging from 0 to 200 ng/mL for free and total PSA. This wearable RDT reader platform running on Google Glass combines a hands-free sensing and image capture interface with powerful servers running our custom image processing codes, and it can be quite useful for real-time spatiotemporal tracking of various diseases and personal medical conditions, providing a valuable tool for epidemiology and mobile health. PMID:24571349

Leveraging High Resolution Topography for Education and Outreach: Updates to OpenTopography to make EarthScope and Other Lidar Datasets more Prominent in Geoscience Education

NASA Astrophysics Data System (ADS)

Kleber, E.; Crosby, C. J.; Arrowsmith, R.; Robinson, S.; Haddad, D. E.

2013-12-01

The use of Light Detection and Ranging (lidar) derived topography has become an indispensable tool in Earth science research. The collection of high-resolution lidar topography from an airborne or terrestrial platform allows landscapes and landforms to be represented at sub-meter resolution and in three dimensions. In addition to its high value for scientific research, lidar derived topography has tremendous potential as a tool for Earth science education. Recent science education initiatives and a community call for access to research-level data make the time ripe to expose lidar data and derived data products as a teaching tool. High resolution topographic data fosters several Disciplinary Core Ideas (DCIs) of the Next Generation Science Standards (NGS, 2013), presents respective Big Ideas of the new community-driven Earth Science Literacy Initiative (ESLI, 2009), teaches to a number National Science Education Standards (NSES, 1996), and Benchmarks for Science Literacy (AAAS, 1993) for science education for undergraduate physical and environmental earth science classes. The spatial context of lidar data complements concepts like visualization, place-based learning, inquiry based teaching and active learning essential to teaching in the geosciences. As official host to EarthScope lidar datasets for tectonically active areas in the western United States, the NSF-funded OpenTopography facility provides user-friendly access to a wealth of data that is easily incorporated into Earth science educational materials. OpenTopography (www.opentopography.org), in collaboration with EarthScope, has developed education and outreach activities to foster teacher, student and researcher utilization of lidar data. These educational resources use lidar data coupled with free tools such as Google Earth to provide a means for students and the interested public to visualize and explore Earth's surface in an interactive manner not possible with most other remotely sensed imagery. The education section of the OpenTopography portal has recently been strengthened with the addition of several new resources and the re-organization of existing content for easy discovery. New resources include a detailed frequently asked questions (FAQ) section, updated 'How-to' videos for downloading data from OpenTopography and additional webpages aimed at students, educators and researchers leveraging existing and updated resources from OpenTopography, EarthScope and other organizations. In addition, the OpenLandform catalog, an online collection of classic geologic landforms depicted in lidar, has been updated to include additional tectonic landforms from EarthScope lidar datasets.

Leveraging Open Standards and Technologies to Search and Display Planetary Image Data

NASA Astrophysics Data System (ADS)

Rose, M.; Schauer, C.; Quinol, M.; Trimble, J.

2011-12-01

Mars and the Moon have both been visited by multiple NASA spacecraft. A large number of images and other data have been gathered by the spacecraft and are publicly available in NASA's Planetary Data System. Through a collaboration with Google, Inc., the User Centered Technologies group at NASA Ames Resarch Center has developed at tool for searching and browsing among images from multiple Mars and Moon missions. Development of this tool was facilitated by the use of several open technologies and standards. First, an open-source full-text search engine is used to search both place names on the target and to find images matching a geographic region. Second, the published API of the Google Earth browser plugin is used to geolocate the images on a virtual globe and allow the user to navigate on the globe to see related images. The structure of the application also employs standard protocols and services. The back-end is exposed as RESTful APIs, which could be reused by other client systems in the future. Further, the communication between the front- and back-end portions of the system utilizes open data standards including XML and KML (Keyhole Markup Language) for representation of textual and geographic data. The creation of the search index was facilitated by reuse of existing, publicly available metadata, including the Gazetteer of Planetary Nomenclature from the USGS, available in KML format. And the image metadata was reused from standards-compliant archives in the Planetary Data System. The system also supports collaboration with other tools by allowing export of search results in KML, and the ability to display those results in the Google Earth desktop application. We will demonstrate the search and visualization capabilities of the system, with emphasis on how the system facilitates reuse of data and services through the adoption of open standards.

Searching for answers to clinical questions using google versus evidence-based summary resources: a randomized controlled crossover study.

PubMed

Kim, Sarang; Noveck, Helaine; Galt, James; Hogshire, Lauren; Willett, Laura; O'Rourke, Kerry

2014-06-01

To compare the speed and accuracy of answering clinical questions using Google versus summary resources. In 2011 and 2012, 48 internal medicine interns from two classes at Rutgers University Robert Wood Johnson Medical School, who had been trained to use three evidence-based summary resources, performed four-minute computer searches to answer 10 clinical questions. Half were randomized to initiate searches for answers to questions 1 to 5 using Google; the other half initiated searches using a summary resource. They then crossed over and used the other resource for questions 6 to 10. They documented the time spent searching and the resource where the answer was found. Time to correct response and percentage of correct responses were compared between groups using t test and general estimating equations. Of 480 questions administered, interns found answers for 393 (82%). Interns initiating searches in Google used a wider variety of resources than those starting with summary resources. No significant difference was found in mean time to correct response (138.5 seconds for Google versus 136.1 seconds for summary resource; P = .72). Mean correct response rate was 58.4% for Google versus 61.5% for summary resource (mean difference -3.1%; 95% CI -10.3% to 4.2%; P = .40). The authors found no significant differences in speed or accuracy between searches initiated using Google versus summary resources. Although summary resources are considered to provide the highest quality of evidence, improvements to allow for better speed and accuracy are needed.

[Google Scholar and the h-index in biomedicine: the popularization of bibliometric assessment].

PubMed

Cabezas-Clavijo, A; Delgado-López-Cózar, E

2013-01-01

The aim of this study is to review the features, benefits and limitations of the new scientific evaluation products derived from Google Scholar, such as Google Scholar Metrics and Google Scholar Citations, as well as the h-index, which is the standard bibliometric indicator adopted by these services. The study also outlines the potential of this new database as a source for studies in Biomedicine, and compares the h-index obtained by the most relevant journals and researchers in the field of intensive care medicine, based on data extracted from the Web of Science, Scopus and Google Scholar. Results show that although the average h-index values in Google Scholar are almost 30% higher than those obtained in Web of Science, and about 15% higher than those collected by Scopus, there are no substantial changes in the rankings generated from one data source or the other. Despite some technical problems, it is concluded that Google Scholar is a valid tool for researchers in Health Sciences, both for purposes of information retrieval and for the computation of bibliometric indicators. Copyright © 2012 Elsevier España, S.L. and SEMICYUC. All rights reserved.

Publishing in the Refereed International Journal of Astronomy & Earth Sciences Education JAESE

NASA Astrophysics Data System (ADS)

Slater, Timothy F.

2015-08-01

Filling a needed scholarly publishing avenue for astronomy education researchers and earth science education researchers, the Journal of Astronomy & Earth Sciences Education- JAESE was first published in 2014. JAESE is a scholarly, peer-reviewed scientific journal publishing original discipline-based education research and evaluation, with an emphasis of significant scientific results derived from ethical observations and systematic experimentation in science education and evaluation. International in scope, JAESE aims to publish the highest quality and timely articles from discipline-based education research that advance understanding of astronomy and earth sciences education and are likely to have a significant impact on the discipline or on policy. Articles are solicited describing both (i) systematic science education research and (ii) evaluated teaching innovations across the broadly defined Earth & space sciences education, including the disciplines of astronomy, climate education, energy resource science, environmental science, geology, geography, agriculture, meteorology, planetary sciences, and oceanography education. The publishing model adopted for this new journal is open-access and articles appear online in GoogleScholar, ERIC, EBSCO, ProQuest, and NASA SAO/ADS and are searchable in catalogs of 440,000 libraries that index online journals of its type. Rather than paid for by library subscriptions or by society membership dues, the annual budget is covered by page-charges paid by individual authors, their institutions, grants or donors: This approach is common in scientific journals, but is relatively uncommon in education journals. Authors retain their own copyright. The journal is owned by the Clute Institute in the United States, which owns and operates 17 scholarly journals and currently edited by former American Astronomical Society Education Officer Tim Slater, who is an endowed professor at the University of Wyoming and a Senior Scientist at the CAPER Center for Astronomy & Physics Education Research. More information about the journal and its policies are available online at http://www.JAESE.org

Development of Waypoint Planning Tool in Response to NASA Field Campaign Challenges

NASA Technical Reports Server (NTRS)

He, Matt; Hardin, Danny; Mayer, Paul; Blakeslee, Richard; Goodman, Michael

2012-01-01

Airborne real time observations are a major component of NASA 's Earth Science research and satellite ground validation studies. Multiple aircraft are involved in most NASA field campaigns. The coordination of the aircraft with satellite overpasses, other airplanes and the constantly evolving, dynamic weather conditions often determines the success of the campaign. Planning a research aircraft mission within the context of meeting the science objectives is a complex task because it requires real time situational awareness of the weather conditions that affect the aircraft track. A flight planning tools is needed to provide situational awareness information to the mission scientists, and help them plan and modify the flight tracks. Scientists at the University of Alabama ]Huntsville and the NASA Marshall Space Flight Center developed the Waypoint Planning Tool, an interactive software tool that enables scientists to develop their own flight plans (also known as waypoints) with point -and-click mouse capabilities on a digital map filled with real time raster and vector data. The development of this Waypoint Planning Tool demonstrates the significance of mission support in responding to the challenges presented during NASA field campaigns. Analysis during and after each campaign helped identify both issues and new requirements, and initiated the next wave of development. Currently the Waypoint Planning Tool has gone through three rounds of development and analysis processes. The development of this waypoint tool is directly affected by the technology advances on GIS/Mapping technologies. From the standalone Google Earth application and simple KML functionalities, to Google Earth Plugin on web platform, and to the rising open source GIS tools with New Java Script frameworks, the Waypoint Planning Tool has entered its third phase of technology advancement. Adapting new technologies for the Waypoint Planning Tool ensures its success in helping scientist reach their mission objectives.

Real Time Filtering of Tweets Using Wikipedia Concepts and Google Tri-gram Semantic Relatedness

DTIC Science & Technology

2015-11-20

Real Time Filtering of Tweets Using Wikipedia Concepts and Google Tri-gram Semantic Relatedness Anh Dang1, Raheleh Makki1, Abidalrahman Moh’d1...of a topic that the user is interested in receiving relevant posts in real-time. Our proposed approach extracts Wikipedia concepts for profiles and...group name “DALTREC”. Our proposed approach for this year’s filtering task is based on using Wikipedia and Google Trigram for calculating the semantic

Advances in Integrating Autonomy with Acoustic Communications for Intelligent Networks of Marine Robots

DTIC Science & Technology

2013-02-01

Sonar AUV #Environmental Sampling Environmental AUV +name : string = OEX Ocean Explorer +name : string = Hammerhead Iver2 +name : string = Unicorn ...executable» Google Earth Bluefin 21 AUV ( Unicorn ) MOOS Computer GPS «serial» Bluefin 21 AUV (Macrura) MOOS Computer «acoustic» Micro-Modem «wired...Computer Bluefin 21 AUV ( Unicorn ) MOOS Computer NURC AUV (OEX) MOOS Computer Topside MOOS Computer «wifi» 5.0GHz WiLan «acoustic» Edgetech GPS

Analysis of the Global Maritime Transportation System and Its Resilience

DTIC Science & Technology

2017-06-01

shortest/cheapest available route. • We establish re-routing strategies that apply, if a part of a route becomes impassable for container ships. We...The currently available throughput data is mostly from 2011, with few exceptions of 2010 and 2012. In total, there are 94 container seaports from 58...port or to the next available container port for further transportation by sea. Figure 3.5. The road layer visualized in Google Earth. Because our

Naval Fuel Management System (NFMS): A Decision Support System for a Limited Resource

DTIC Science & Technology

2010-09-01

Figure 14. Proof of concept displayed on Google Earth. ..................................................50 Figure 15. GTG fuel burn rate. From [10...the gas turbine generators ( GTG ) to provide electricity. An average of 280 gallons per hour (GPH) was used for the GTG to take into account changing... GTGs or operating on more than one GTG for short periods of time. The amount of fuel burned by the GTGs was found in the Shipboard Energy

Towards Large-area Field-scale Operational Evapotranspiration for Water Use Mapping

NASA Astrophysics Data System (ADS)

Senay, G. B.; Friedrichs, M.; Morton, C.; Huntington, J. L.; Verdin, J.

2017-12-01

Field-scale evapotranspiration (ET) estimates are needed for improving surface and groundwater use and water budget studies. Ideally, field-scale ET estimates would be at regional to national levels and cover long time periods. As a result of large data storage and computational requirements associated with processing field-scale satellite imagery such as Landsat, numerous challenges remain to develop operational ET estimates over large areas for detailed water use and availability studies. However, the combination of new science, data availability, and cloud computing technology is enabling unprecedented capabilities for ET mapping. To demonstrate this capability, we used Google's Earth Engine cloud computing platform to create nationwide annual ET estimates with 30-meter resolution Landsat ( 16,000 images) and gridded weather data using the Operational Simplified Surface Energy Balance (SSEBop) model in support of the National Water Census, a USGS research program designed to build decision support capacity for water management agencies and other natural resource managers. By leveraging Google's Earth Engine Application Programming Interface (API) and developing software in a collaborative, open-platform environment, we rapidly advance from research towards applications for large-area field-scale ET mapping. Cloud computing of the Landsat image archive combined with other satellite, climate, and weather data, is creating never imagined opportunities for assessing ET model behavior and uncertainty, and ultimately providing the ability for more robust operational monitoring and assessment of water use at field-scales.

Regional early flood warning system: design and implementation

NASA Astrophysics Data System (ADS)

Chang, L. C.; Yang, S. N.; Kuo, C. L.; Wang, Y. F.

2017-12-01

This study proposes a prototype of the regional early flood inundation warning system in Tainan City, Taiwan. The AI technology is used to forecast multi-step-ahead regional flood inundation maps during storm events. The computing time is only few seconds that leads to real-time regional flood inundation forecasting. A database is built to organize data and information for building real-time forecasting models, maintaining the relations of forecasted points, and displaying forecasted results, while real-time data acquisition is another key task where the model requires immediately accessing rain gauge information to provide forecast services. All programs related database are constructed in Microsoft SQL Server by using Visual C# to extracting real-time hydrological data, managing data, storing the forecasted data and providing the information to the visual map-based display. The regional early flood inundation warning system use the up-to-date Web technologies driven by the database and real-time data acquisition to display the on-line forecasting flood inundation depths in the study area. The friendly interface includes on-line sequentially showing inundation area by Google Map, maximum inundation depth and its location, and providing KMZ file download of the results which can be watched on Google Earth. The developed system can provide all the relevant information and on-line forecast results that helps city authorities to make decisions during typhoon events and make actions to mitigate the losses.

Presidential Citation for Science and Society

NASA Astrophysics Data System (ADS)

2012-07-01

AGU presented its Presidential Citation for Science and Society to three recipients at a reception on 1 May 2012 in the Rayburn House Office Building as part of the inaugural AGU Science Policy Conference. Google Earth, Jane Lubchenco, who is the under secretary of Commerce for oceans and atmosphere and administrator of the National Oceanic and Atmospheric Administration, and Sen. Olympia Snowe (R-Maine) were recognized for their leadership and vision in shaping policy and heightening public awareness of the value of Earth and space science. “This is an important award because with it AGU brings to light the importance of cutting-edge use-inspired science that helps people, communities, and businesses adapt to climate change and sustainably manage our oceans and coasts,” Lubchenco said.

Open Land-Use Map: A Regional Land-Use Mapping Strategy for Incorporating OpenStreetMap with Earth Observations

NASA Astrophysics Data System (ADS)

Yang, D.; Fu, C. S.; Binford, M. W.

2017-12-01

The southeastern United States has high landscape heterogeneity, withheavily managed forestlands, highly developed agriculture lands, and multiple metropolitan areas. Human activities are transforming and altering land patterns and structures in both negative and positive manners. A land-use map for at the greater scale is a heavy computation task but is critical to most landowners, researchers, and decision makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating the classification maps at the regional scale: the necessity of large training point sets and the expensive computation cost-in terms of both money and time-in classifier modeling. Volunteered Geographic Information (VGI) opens a new era in mapping and visualizing our world, where the platform is open for collecting valuable georeferenced information by volunteer citizens, and the data is freely available to the public. As one of the most well-known VGI initiatives, OpenStreetMap (OSM) contributes not only road network distribution, but also the potential for using this data to justify land cover and land use classifications. Google Earth Engine (GEE) is a platform designed for cloud-based mapping with a robust and fast computing power. Most large scale and national mapping approaches confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover datasets. Unlike most other large-scale approaches, we distinguish and differentiate land-use from land cover. By focusing our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach is developed by incorporating the OpenstreepMap dataset into Earth observation remote sensing imageries instead of the often-used land cover base maps.

Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

NASA Astrophysics Data System (ADS)

Shew, A. M.; Ghosh, A.

2017-10-01

Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

Technical Aspects for the Creation of a Multi-Dimensional Land Information System

NASA Astrophysics Data System (ADS)

Ioannidis, Charalabos; Potsiou, Chryssy; Soile, Sofia; Verykokou, Styliani; Mourafetis, George; Doulamis, Nikolaos

2016-06-01

The complexity of modern urban environments and civil demands for fast, reliable and affordable decision-making requires not only a 3D Land Information System, which tends to replace traditional 2D LIS architectures, but also the need to address the time and scale parameters, that is, the 3D geometry of buildings in various time instances (4th dimension) at various levels of detail (LoDs - 5th dimension). This paper describes and proposes solutions for technical aspects that need to be addressed for the 5D modelling pipeline. Such solutions include the creation of a 3D model, the application of a selective modelling procedure between various time instances and at various LoDs, enriched with cadastral and other spatial data, and a procedural modelling approach for the representation of the inner parts of the buildings. The methodology is based on automatic change detection algorithms for spatial-temporal analysis of the changes that took place in subsequent time periods, using dense image matching and structure from motion algorithms. The selective modelling approach allows a detailed modelling only for the areas where spatial changes are detected. The procedural modelling techniques use programming languages for the textual semantic description of a building; they require the modeller to describe its part-to-whole relationships. Finally, a 5D viewer is developed, in order to tackle existing limitations that accompany the use of global systems, such as the Google Earth or the Google Maps, as visualization software. An application based on the proposed methodology in an urban area is presented and it provides satisfactory results.

Survey of Quantification and Distance Functions Used for Internet-based Weak-link Sociological Phenomena

DTIC Science & Technology

2016-03-01

well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology literature for the relevant...Google matrix, PageRank as well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology...The PI studied all mathematical literature he can find related to the Google search engine, Google matrix, PageRank as well as the Yahoo search

A System for Traffic Violation Detection

PubMed Central

Aliane, Nourdine; Fernandez, Javier; Mata, Mario; Bemposta, Sergio

2014-01-01

This paper describes the framework and components of an experimental platform for an advanced driver assistance system (ADAS) aimed at providing drivers with a feedback about traffic violations they have committed during their driving. The system is able to detect some specific traffic violations, record data associated to these faults in a local data-base, and also allow visualization of the spatial and temporal information of these traffic violations in a geographical map using the standard Google Earth tool. The test-bed is mainly composed of two parts: a computer vision subsystem for traffic sign detection and recognition which operates during both day and nighttime, and an event data recorder (EDR) for recording data related to some specific traffic violations. The paper covers firstly the description of the hardware architecture and then presents the policies used for handling traffic violations. PMID:25421737

A system for traffic violation detection.

PubMed

Aliane, Nourdine; Fernandez, Javier; Mata, Mario; Bemposta, Sergio

2014-11-24

This paper describes the framework and components of an experimental platform for an advanced driver assistance system (ADAS) aimed at providing drivers with a feedback about traffic violations they have committed during their driving. The system is able to detect some specific traffic violations, record data associated to these faults in a local data-base, and also allow visualization of the spatial and temporal information of these traffic violations in a geographical map using the standard Google Earth tool. The test-bed is mainly composed of two parts: a computer vision subsystem for traffic sign detection and recognition which operates during both day and nighttime, and an event data recorder (EDR) for recording data related to some specific traffic violations. The paper covers firstly the description of the hardware architecture and then presents the policies used for handling traffic violations.

EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection.

PubMed

Aanensen, David M; Huntley, Derek M; Feil, Edward J; al-Own, Fada'a; Spratt, Brian G

2009-09-16

Epidemiologists and ecologists often collect data in the field and, on returning to their laboratory, enter their data into a database for further analysis. The recent introduction of mobile phones that utilise the open source Android operating system, and which include (among other features) both GPS and Google Maps, provide new opportunities for developing mobile phone applications, which in conjunction with web applications, allow two-way communication between field workers and their project databases. Here we describe a generic framework, consisting of mobile phone software, EpiCollect, and a web application located within www.spatialepidemiology.net. Data collected by multiple field workers can be submitted by phone, together with GPS data, to a common web database and can be displayed and analysed, along with previously collected data, using Google Maps (or Google Earth). Similarly, data from the web database can be requested and displayed on the mobile phone, again using Google Maps. Data filtering options allow the display of data submitted by the individual field workers or, for example, those data within certain values of a measured variable or a time period. Data collection frameworks utilising mobile phones with data submission to and from central databases are widely applicable and can give a field worker similar display and analysis tools on their mobile phone that they would have if viewing the data in their laboratory via the web. We demonstrate their utility for epidemiological data collection and display, and briefly discuss their application in ecological and community data collection. Furthermore, such frameworks offer great potential for recruiting 'citizen scientists' to contribute data easily to central databases through their mobile phone.

A Google-based approach for monitoring suicide risk.

PubMed

Solano, Paola; Ustulin, Morena; Pizzorno, Enrico; Vichi, Monica; Pompili, Maurizio; Serafini, Gianluca; Amore, Mario

2016-12-30

People seeking information and news regarding suicide are likely to use the Internet. However, evidence of the relationship between suicide-related search volumes and national suicide-rates in different countries can be strikingly different. We aimed to investigate the relationship between suicide-rates and Google suicide-related search volumes in the Italian population (2008-2012) using the Italian mortality database that provided monthly national data concerning suicides (2008-2012). Moreover, this study aimed to identify future trends of national suicide rates on the basis of the results we obtained concerning the period 2013-14. Google Trends provided data of online monthly search-volumes of the term "suicide", "commit suicide" and "how to commit suicide" in Google Search and Google News (2008-2014). Google Search volumes for the term "suicide" lags suicide by three months (ρ=0.482, p-value<0.001), whereas no correlation was found between search volumes for "commit suicide" and "how to commit suicide" and national suicide rates. Google News search volumes for the three terms resulted in white noise. Apparently, online searches for suicide-related terms in Italy are more likely to be linked to factors other than suicidiality such as personal interest and suicide bereavement. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

ICTNET at Microblog Track TREC 2012

DTIC Science & Technology

2012-11-01

Weight(T) = 1 _(()) ∗ ∑ () ∗ () ∑ ()∈ () In external expansion, we use Google ...based on language model, we choose stupid backoff as the smoothing technique and “queue” as the history retention technique[7].In the filter based on...is used in ICTWDSERUN2. In both ICTWDSERUN3 and ICTWDSERUN4, we use google search results as query expansion. RankSVMmethod is used in both

[Electronic poison information management system].

PubMed

Kabata, Piotr; Waldman, Wojciech; Kaletha, Krystian; Sein Anand, Jacek

2013-01-01

We describe deployment of electronic toxicological information database in poison control center of Pomeranian Center of Toxicology. System was based on Google Apps technology, by Google Inc., using electronic, web-based forms and data tables. During first 6 months from system deployment, we used it to archive 1471 poisoning cases, prepare monthly poisoning reports and facilitate statistical analysis of data. Electronic database usage made Poison Center work much easier.

Tree Canopy Light Interception Estimates in Almond and a Walnut Orchards Using Ground, Low Flying Aircraft, and Satellite Based Methods to Improve Irrigation Scheduling Programs

NASA Technical Reports Server (NTRS)

Rosecrance, Richard C.; Johnson, Lee; Soderstrom, Dominic

2016-01-01

Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

Tree canopy light interception estimates in almond and a walnut orchards using ground, low flying aircraft, and satellite based methods to improve irrigation scheduling programs.

NASA Astrophysics Data System (ADS)

Rosecrance, R. C.; Johnson, L.; Soderstrom, D.

2016-12-01

Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

NASA's Earth Science Gateway: A Platform for Interoperable Services in Support of the GEOSS Architecture

NASA Astrophysics Data System (ADS)

Alameh, N.; Bambacus, M.; Cole, M.

2006-12-01

Nasa's Earth Science as well as interdisciplinary research and applications activities require access to earth observations, analytical models and specialized tools and services, from diverse distributed sources. Interoperability and open standards for geospatial data access and processing greatly facilitate such access among the information and processing compo¬nents related to space¬craft, airborne, and in situ sensors; predictive models; and decision support tools. To support this mission, NASA's Geosciences Interoperability Office (GIO) has been developing the Earth Science Gateway (ESG; online at http://esg.gsfc.nasa.gov) by adapting and deploying a standards-based commercial product. Thanks to extensive use of open standards, ESG can tap into a wide array of online data services, serve a variety of audiences and purposes, and adapt to technology and business changes. Most importantly, the use of open standards allow ESG to function as a platform within a larger context of distributed geoscience processing, such as the Global Earth Observing System of Systems (GEOSS). ESG shares the goals of GEOSS to ensure that observations and products shared by users will be accessible, comparable, and understandable by relying on common standards and adaptation to user needs. By maximizing interoperability, modularity, extensibility and scalability, ESG's architecture fully supports the stated goals of GEOSS. As such, ESG's role extends beyond that of a gateway to NASA science data to become a shared platform that can be leveraged by GEOSS via: A modular and extensible architecture Consensus and community-based standards (e.g. ISO and OGC standards) A variety of clients and visualization techniques, including WorldWind and Google Earth A variety of services (including catalogs) with standard interfaces Data integration and interoperability Mechanisms for user involvement and collaboration Mechanisms for supporting interdisciplinary and domain-specific applications ESG has played a key role in recent GEOSS Service Network (GSN) demos and workshops, acting not only as a service and data catalog and discovery client, but also as a portrayal and visualization client to distributed data.

Teaching Young Adults with Intellectual and Developmental Disabilities Community-Based Navigation Skills to Take Public Transportation.

PubMed

Price, Richard; Marsh, Abbie J; Fisher, Marisa H

2018-03-01

Facilitating the use of public transportation enhances opportunities for independent living and competitive, community-based employment for individuals with intellectual and developmental disabilities (IDD). Four young adults with IDD were taught through total-task chaining to use the Google Maps application, a self-prompting, visual navigation system, to take the bus to locations around a college campus and the community. Three of four participants learned to use Google Maps to independently navigate public transportation. Google Maps may be helpful in supporting independent travel, highlighting the importance of future research in teaching navigation skills. Learning to independently use public transportation increases access to autonomous activities, such as opportunities to work and to attend postsecondary education programs on large college campuses.Individuals with IDD can be taught through chaining procedures to use the Google Maps application to navigate public transportation.Mobile map applications are an effective and functional modern tool that can be used to teach community navigation.

TCL2 Ocean Scenario Replay

NASA Technical Reports Server (NTRS)

Mohlenbrink, Christoph P.; Omar, Faisal Gamal; Homola, Jeffrey R.

2017-01-01

This is a video replay of system data that was generated from the UAS Traffic Management (UTM) Technical Capability Level (TCL) 2 flight demonstration in Nevada and rendered in Google Earth. What is depicted in the replay is a particular set of flights conducted as part of what was referred to as the Ocean scenario. The test range and surrounding area are presented followed by an overview of operational volumes. System messaging is also displayed as well as a replay of all of the five test flights as they occurred.

Smartphones and Time Zones

NASA Astrophysics Data System (ADS)

Baird, William; Secrest, Jeffery; Padgett, Clifford; Johnson, Wayne; Hagrelius, Claire

2016-09-01

Using the Sun to tell time is an ancient idea, but we can take advantage of modern technology to bring it into the 21st century for students in astronomy, physics, or physical science classes. We have employed smartphones, Google Earth, and 3D printing to find the moment of local noon at two widely separated locations. By reviewing GPS time-stamped photos from each place, we are able to illustrate that local noon is longitude-dependent and therefore explain the need for time zones.

Infusion of a Gaming Paradigm into Computer-Aided Engineering Design Tools

DTIC Science & Technology

2012-05-03

Virtual Test Bed (VTB), and the gaming tool, Unity3D . This hybrid gaming environment coupled a three-dimensional (3D) multibody vehicle system model...from Google Earth to the 3D visual front-end fabricated around Unity3D . The hybrid environment was sufficiently developed to support analyses of the...ndFr Cti3r4 G’OjrdFr ctior-2 The VTB simulation of the vehicle dynamics ran concurrently with and interacted with the gaming engine, Unity3D which

Geodynamics in Modular Course System at Vienna High School

NASA Astrophysics Data System (ADS)

Pitzl-Reinbacher, Robert

2017-04-01

In Austria there are currently some major reforms concerning high school education underway. At our school, the Bundesgymnasium and Bundesrealgymnasium Draschestrasse, a school belonging to the Vienna Bilingual Schooling branch, we have developed a course system in which pupils can select courses and determine individually which areas of study they want to focus on. Specially devised courses have been developed which fit within the framework of natural and applied sciences but go beyond the basic curriculum in physics. Geodynamics is the title of one of these courses, with an emphasis on weather, climate and geodynamic processes of the earth's crust. The course „The restless earth" deals specifically with plate tectonics, vulcanism, formation of mountains and processes such as ocean currents and the physics involved. Apart from theoretical basics we use manifold media and approaches concerning visualization: graphics, map data taken from Google Maps, satellite pictures, and others. The knowledge acquired in this course is broadened and consolidated by means of excursions to the Vienna Natural History Museum where additional instructional materials and visual aids are on display. Based on this experience pupils are requested to hold presentations (individually or in groups) at the end of the course.

Using Google Maps to Access USGS Volcano Hazards Information

NASA Astrophysics Data System (ADS)

Venezky, D. Y.; Snedigar, S.; Guffanti, M.; Bailey, J. E.; Wall, B. G.

2006-12-01

The U.S. Geological Survey (USGS) Volcano Hazard Program (VHP) is revising the information architecture of our website to provide data within a geospatial context for emergency managers, educators, landowners in volcanic areas, researchers, and the general public. Using a map-based interface for displaying hazard information provides a synoptic view of volcanic activity along with the ability to quickly ascertain where hazards are in relation to major population and infrastructure centers. At the same time, the map interface provides a gateway for educators and the public to find information about volcanoes in their geographic context. A plethora of data visualization solutions are available that are flexible, customizable, and can be run on individual websites. We are currently using a Google map interface because it can be accessed immediately from a website (a downloadable viewer is not required), and it provides simple features for moving around and zooming within the large map area that encompasses U.S. volcanism. A text interface will also be available. The new VHP website will serve as a portal to information for each volcano the USGS monitors with icons for alert levels and aviation color codes. When a volcano is clicked, a window will provide additional information including links to maps, images, and real-time data, thereby connecting information from individual observatories, the Smithsonian Institution, and our partner universities. In addition to the VHP home page, many observatories and partners have detailed graphical interfaces to data and images that include the activity pages for the Alaska Volcano Observatory, the Smithsonian Google Earth files, and Yellowstone Volcano Observatory pictures and data. Users with varied requests such as raw data, scientific papers, images, or brief overviews expect to be able to quickly access information for their specialized needs. Over the next few years we will be gathering, cleansing, reorganizing, and posting data in multiple formats to meet these needs.

Smarter Earth Science Data System

NASA Technical Reports Server (NTRS)

Huang, Thomas

2013-01-01

The explosive growth in Earth observational data in the recent decade demands a better method of interoperability across heterogeneous systems. The Earth science data system community has mastered the art in storing large volume of observational data, but it is still unclear how this traditional method scale over time as we are entering the age of Big Data. Indexed search solutions such as Apache Solr (Smiley and Pugh, 2011) provides fast, scalable search via keyword or phases without any reasoning or inference. The modern search solutions such as Googles Knowledge Graph (Singhal, 2012) and Microsoft Bing, all utilize semantic reasoning to improve its accuracy in searches. The Earth science user community is demanding for an intelligent solution to help them finding the right data for their researches. The Ontological System for Context Artifacts and Resources (OSCAR) (Huang et al., 2012), was created in response to the DARPA Adaptive Vehicle Make (AVM) programs need for an intelligent context models management system to empower its terrain simulation subsystem. The core component of OSCAR is the Environmental Context Ontology (ECO) is built using the Semantic Web for Earth and Environmental Terminology (SWEET) (Raskin and Pan, 2005). This paper presents the current data archival methodology within a NASA Earth science data centers and discuss using semantic web to improve the way we capture and serve data to our users.

Mechanical analysis of the dry stone walls built by the Incas

NASA Astrophysics Data System (ADS)

Castro, Jaime; Vallejo, Luis E.; Estrada, Nicolas

2017-06-01

In this paper, the retaining walls in the agricultural terraces built by the Incas are analyzed from a mechanical point of view. In order to do so, ten different walls from the Lower Agricultural Sector of Machu Picchu, Perú, were selected using images from Google Street View and Google Earth Pro. Then, these walls were digitalized and their mechanical stability was evaluated. Firstly, it was found that these retaining walls are characterized by two distinctive features: disorder and a block size distribution with a large size span, i.e., the particle size varies from blocks that can be carried by one person to large blocks weighing several tons. Secondly, it was found that, thanks to the large span of the block size distribution, the factor of safety of the Inca retaining walls is remarkably close to those that are recommended in modern geotechnical design standards. This suggests that these structures were not only functional but also highly optimized, probably as a result of a careful trial and error procedure.

Rage against the machine? Google's self-driving cars versus human drivers.

PubMed

Teoh, Eric R; Kidd, David G

2017-12-01

Automated driving represents both challenges and opportunities in highway safety. Google has been developing self-driving cars and testing them under employee supervision on public roads since 2009. These vehicles have been involved in several crashes, and it is of interest how this testing program compares to human drivers in terms of safety. Google car crashes were coded by type and severity based on narratives released by Google. Crash rates per million vehicle miles traveled (VMT) were computed for crashes deemed severe enough to be reportable to police. These were compared with police-reported crash rates for human drivers. Crash types also were compared. Google cars had a much lower rate of police-reportable crashes per million VMT than human drivers in Mountain View, Calif., during 2009-2015 (2.19 vs 6.06), but the difference was not statistically significant. The most common type of collision involving Google cars was when they got rear-ended by another (human-driven) vehicle. Google cars shared responsibility for only one crash. These results suggest Google self-driving cars, while a test program, are safer than conventional human-driven passenger vehicles; however, currently there is insufficient information to fully examine the extent to which disengagements affected these results. Results suggest that highly-automated vehicles can perform more safely than human drivers in certain conditions, but will continue to be involved in crashes with conventionally-driven vehicles. Copyright © 2017. Published by Elsevier Ltd.

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Brown De Colstoun, Eric C.

2017-01-01

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Laidlaw, R.; Painter, T. H.; Mattmann, C. A.; Ramirez, P.; Bormann, K.; Brodzik, M. J.; Burgess, A. B.; Rittger, K.; Goodale, C. E.; Joyce, M.; McGibbney, L. J.; Zimdars, P.

2014-12-01

NASA JPL's Snow Data System has a data-processing pipeline powered by Apache OODT, an open source software tool. The pipeline has been running for several years and has successfully generated a significant amount of cryosphere data, including MODIS-based products such as MODSCAG, MODDRFS and MODICE, with historical and near-real time windows and covering regions such as the Artic, Western US, Alaska, Central Europe, Asia, South America, Australia and New Zealand. The team continues to improve the pipeline, using monitoring tools such as Ganglia to give an overview of operations, and improving fault-tolerance with automated recovery scripts. Several alternative adaptations of the Snow Covered Area and Grain size (SCAG) algorithm are being investigated. These include using VIIRS and Landsat TM/ETM+ satellite data as inputs. Parallel computing techniques are being considered for core SCAG processing, such as using the PyCUDA Python API to utilize multi-core GPU architectures. An experimental version of MODSCAG is also being developed for the Google Earth Engine platform, a cloud-based service.

Results of Joint Observations of Jupiter's Atmosphere by Juno and a Network of Earth-Based Observing Stations

NASA Astrophysics Data System (ADS)

Orton, G. S.; Momary, T.; Tabataba-Vakili, F.; Bolton, S.; Levin, S.; Adriani, A.; Gladstone, G. R.; Hansen, C. J.; Janssen, M.

2017-09-01

Well over sixty investigator/instrument investigations are actively engaged in the support of the Juno mission. These observations range from X-ray to the radio wavelengths and involve both space- and ground-based astronomical facilities. These observations enhance and expand Juno measurements by (1) providing a context that expands the area covered by often narrow spatial coverage of Juno's instruments, (2) providing a temporal context that shows how phenomena evolve over Juno's 53-day orbit period, (3) providing observations in spectral ranges not covered by Juno's instruments, and (4) monitoring the behavior of external influences to Jupiter's magnetosphere. Intercommunication between the Juno scientists and the support program is maintained by reference to a Google table that describes the observation and its current status, as well as by occasional group emails. A non-interactive version of this invitation-only site is mirrored in a public site. Several sets of these supporting observations are described at this meeting.

IDP camp evolvement analysis in Darfur using VHSR optical satellite image time series and scientific visualization on virtual globes

NASA Astrophysics Data System (ADS)

Tiede, Dirk; Lang, Stefan

2010-11-01

In this paper we focus on the application of transferable, object-based image analysis algorithms for dwelling extraction in a camp for internally displaced people (IDP) in Darfur, Sudan along with innovative means for scientific visualisation of the results. Three very high spatial resolution satellite images (QuickBird: 2002, 2004, 2008) were used for: (1) extracting different types of dwellings and (2) calculating and visualizing added-value products such as dwelling density and camp structure. The results were visualized on virtual globes (Google Earth and ArcGIS Explorer) revealing the analysis results (analytical 3D views,) transformed into the third dimension (z-value). Data formats depend on virtual globe software including KML/KMZ (keyhole mark-up language) and ESRI 3D shapefiles streamed as ArcGIS Server-based globe service. In addition, means for improving overall performance of automated dwelling structures using grid computing techniques are discussed using examples from a similar study.

Data to Pictures to Data: Outreach Imaging Software and Metadata

NASA Astrophysics Data System (ADS)

Levay, Z.

2011-07-01

A convergence between astronomy science and digital photography has enabled a steady stream of visually rich imagery from state-of-the-art data. The accessibility of hardware and software has facilitated an explosion of astronomical images for outreach, from space-based observatories, ground-based professional facilities and among the vibrant amateur astrophotography community. Producing imagery from science data involves a combination of custom software to understand FITS data (FITS Liberator), off-the-shelf, industry-standard software to composite multi-wavelength data and edit digital photographs (Adobe Photoshop), and application of photo/image-processing techniques. Some additional effort is needed to close the loop and enable this imagery to be conveniently available for various purposes beyond web and print publication. The metadata paradigms in digital photography are now complying with FITS and science software to carry information such as keyword tags and world coordinates, enabling these images to be usable in more sophisticated, imaginative ways exemplified by Sky in Google Earth and World Wide Telescope.

Ship detection from high-resolution imagery based on land masking and cloud filtering

NASA Astrophysics Data System (ADS)

Jin, Tianming; Zhang, Junping

2015-12-01

High resolution satellite images play an important role in target detection application presently. This article focuses on the ship target detection from the high resolution panchromatic images. Taking advantage of geographic information such as the coastline vector data provided by NOAA Medium Resolution Coastline program, the land region is masked which is a main noise source in ship detection process. After that, the algorithm tries to deal with the cloud noise which appears frequently in the ocean satellite images, which is another reason for false alarm. Based on the analysis of cloud noise's feature in frequency domain, we introduce a windowed noise filter to get rid of the cloud noise. With the help of morphological processing algorithms adapted to target detection, we are able to acquire ship targets in fine shapes. In addition, we display the extracted information such as length and width of ship targets in a user-friendly way i.e. a KML file interpreted by Google Earth.

Juicebox.js Provides a Cloud-Based Visualization System for Hi-C Data.

PubMed

Robinson, James T; Turner, Douglass; Durand, Neva C; Thorvaldsdóttir, Helga; Mesirov, Jill P; Aiden, Erez Lieberman

2018-02-28

Contact mapping experiments such as Hi-C explore how genomes fold in 3D. Here, we introduce Juicebox.js, a cloud-based web application for exploring the resulting datasets. Like the original Juicebox application, Juicebox.js allows users to zoom in and out of such datasets using an interface similar to Google Earth. Juicebox.js also has many features designed to facilitate data reproducibility and sharing. Furthermore, Juicebox.js encodes the exact state of the browser in a shareable URL. Creating a public browser for a new Hi-C dataset does not require coding and can be accomplished in under a minute. The web app also makes it possible to create interactive figures online that can complement or replace ordinary journal figures. When combined with Juicer, this makes the entire process of data analysis transparent, insofar as every step from raw reads to published figure is publicly available as open source code. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

The Fold Analysis Challenge: A virtual globe-based educational resource

NASA Astrophysics Data System (ADS)

De Paor, Declan G.; Dordevic, Mladen M.; Karabinos, Paul; Tewksbury, Barbara J.; Whitmeyer, Steven J.

2016-04-01

We present an undergraduate structural geology laboratory exercise using the Google Earth virtual globe with COLLADA models, optionally including an interactive stereographic projection and JavaScript controls. The learning resource challenges students to identify bedding traces and estimate bedding orientation at several locations on a fold, to fit the fold axis and axial plane to stereographic projection data, and to fit a doubly-plunging fold model to the large-scale structure. The chosen fold is the Sheep Mountain Anticline, a Laramide uplift in the Big Horn Basin of Wyoming. We take an education research-based approach, guiding students through three levels of difficulty. The exercise aims to counter common student misconceptions and stumbling blocks regarding penetrative structures. It can be used in preparation for an in-person field trip, for post-trip reinforcement, or as a virtual field experience in an online-only course. Our KML scripts can be easily transferred to other fold structures around the globe.

Dr Google

PubMed Central

Pías-Peleteiro, Leticia; Cortés-Bordoy, Javier; Martinón-Torres, Federico

2013-01-01

Objectives: To assess and analyze the information and recommendations provided by Google Web Search™ (Google) in relation to web searches on the HPV vaccine, indications for females and males and possible adverse effects. Materials and Methods: Descriptive cross-sectional study of the results of 14 web searches. Comprehensive analysis of results based on general recommendation given (favorable/dissuasive), as well as compliance with pre-established criteria, namely design, content and credibility. Sub-analysis of results according to site category: general information, blog / forum and press. Results: In the comprehensive analysis of results, 72.2% of websites offer information favorable to HPV vaccination, with varying degrees of content detail, vs. 27.8% with highly dissuasive content in relation to HPV vaccination. The most frequent type of site is the blog or forum. The information found is frequently incomplete, poorly structured, and often lacking in updates, bibliography and adequate citations, as well as sound credibility criteria (scientific association accreditation and/or trust mark system). Conclusions: Google, as a tool which users employ to locate medical information and advice, is not specialized in providing information that is necessarily rigorous or valid from a scientific perspective. Search results and ranking based on Google's generalized algorithms can lead users to poorly grounded opinions and statements, which may impact HPV vaccination perception and subsequent decision making. PMID:23744505

The Adversarial Route Analysis Tool: A Web Application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casson, William H. Jr.

2012-08-02

The Adversarial Route Analysis Tool is a type of Google maps for adversaries. It's a web-based Geospatial application similar to Google Maps. It helps the U.S. government plan operations that predict where an adversary might be. It's easily accessible and maintainble and it's simple to use without much training.

(Meta)Search like Google

ERIC Educational Resources Information Center

Rochkind, Jonathan

2007-01-01

The ability to search and receive results in more than one database through a single interface--or metasearch--is something many users want. Google Scholar--the search engine of specifically scholarly content--and library metasearch products like Ex Libris's MetaLib, Serials Solution's Central Search, WebFeat, and products based on MuseGlobal used…

Comparing the demands of destination entry using Google Glass and the Samsung Galaxy S4 during simulated driving.

PubMed

Beckers, Niek; Schreiner, Sam; Bertrand, Pierre; Mehler, Bruce; Reimer, Bryan

2017-01-01

The relative impact of using a Google Glass based voice interface to enter a destination address compared to voice and touch-entry methods using a handheld Samsung Galaxy S4 smartphone was assessed in a driving simulator. Voice entry (Google Glass and Samsung) had lower subjective workload ratings, lower standard deviation of lateral lane position, shorter task durations, faster remote Detection Response Task (DRT) reaction times, lower DRT miss rates, and resulted in less time glancing off-road than the primary visual-manual interaction with the Samsung Touch interface. Comparing voice entry methods, using Google Glass took less time, while glance metrics and reaction time to DRT events responded to were similar. In contrast, DRT miss rate was higher for Google Glass, suggesting that drivers may be under increased distraction levels but for a shorter period of time; whether one or the other equates to an overall safer driving experience is an open question. Copyright © 2016 Elsevier Ltd. All rights reserved.

Examples to Keep the Passion for the Geosciences

NASA Astrophysics Data System (ADS)

Fernández Raga, María; Palencia Coto, Covadonga; Cerdà, Artemi

2014-05-01

It is said that the beasts can smell fear. The translation to education is that students know when our vocation is really teaching or when you are teaching as a result of the sequence of events like a side effect of an investigating vocation path. But to become a good teacher, you need to love teaching!!! Education work requires a dynamic appeal by the students. It should be entertaining, motivating, interactive and dynamic. In this session I will present several tips and examples to get attention on your geology sessions: 1. The teacher should maintain a high interest in the subject of your work. Motivation is contagious!!!!If you show passion the other will feel it. 2. Change the attitude of students. Some activities can help you to do that like asking for the preparation of an experiment, and analyzing the results. Some examples will be shown. 3. Arouse the curiosity of the students. Some strategies could be asking questions in novel, controversial or inconsistent ways, asking conceptual conflicts and paradox that looks not expected from what is studied or 4. Use some tools to get the attention of the students. Examples of these tools can be Google Maps and Google Earth (teaching them to design routes and marking studies), Google drive (to create documents online in a team and file sharing), Google plus (to hang interesting news). 5. Examine students each week. Although it will be laborious, their work and learning will be more gradual. 6. Increase levels of competition among peers. 7. Relate what you know with what you learn. It is very important to be aware of the basis on which pupils, through prior knowledge test match. 8. Feel competent. Teacher's confidence is vital when teaching a class. You must be aware of our weaknesses and humble, but our nerves should help us to improve the quality of our classes. 9. Individualized teaching and learning. Numerous psychological and sociological studies suggest that the existence of social networks contribute to the welfare and health of the person. Applying this idea to the field of training, promote development within the classroom social networking encourages participation and aid in student learning.The criterion to consider dissolving or enhance these natural groups is given by the adequacy or not the educational proposed approaches (objectives, content, interests , etc.). And last but not least… 10. Never stop learning!!!!!!!!! Teaching geosciences needs passion for the Earth, the processes, the forms…And to show this in the field to the students.

Correlation between National Influenza Surveillance Data and Google Trends in South Korea

PubMed Central

Jo, Min Woo; Shin, Soo-Yong; Lee, Jae Ho; Ryoo, Seoung Mok; Kim, Won Young; Seo, Dong-Woo

2013-01-01

Background In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea. Methods Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson's correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons. Results The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05). Conclusions In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends. PMID:24339927

Correlation between national influenza surveillance data and google trends in South Korea.

PubMed

Cho, Sungjin; Sohn, Chang Hwan; Jo, Min Woo; Shin, Soo-Yong; Lee, Jae Ho; Ryoo, Seoung Mok; Kim, Won Young; Seo, Dong-Woo

2013-01-01

In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea. Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson's correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons. The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05). In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.

Digital Earth - Young generation's comprehension and ideas

NASA Astrophysics Data System (ADS)

Bandrova, T.; Konecny, M.

2014-02-01

The authors are experienced in working with children and students in the field of early warning and crises management and cartography. All these topics are closely connected to Digital Earth (DE) ideas. On the basis of a questionnaire, the young generation's comprehension of DE concept is clarified. Students from different age groups (from 19 to 36) from different countries and with different social, cultural, economical and political backgrounds are asked to provide definition of DE and describe their basic ideas about meaning, methodology and applications of the concept. The questions aim to discover the young generation's comprehension of DE ideas. They partially cover the newest trends of DE development like social, cultural and environmental issues as well as the styles of new communications (Google Earth, Facebook, LinkedIn, etc.). In order to assure the future development of the DE science, it is important to take into account the young generation's expectations. Some aspects of DE development are considered in the Conclusions.

Urban topography for flood modeling by fusion of OpenStreetMap, SRTM and local knowledge

NASA Astrophysics Data System (ADS)

Winsemius, Hessel; Donchyts, Gennadii; Eilander, Dirk; Chen, Jorik; Leskens, Anne; Coughlan, Erin; Mawanda, Shaban; Ward, Philip; Diaz Loaiza, Andres; Luo, Tianyi; Iceland, Charles

2016-04-01

Topography data is essential for understanding and modeling of urban flood hazard. Within urban areas, much of the topography is defined by highly localized man-made features such as roads, channels, ditches, culverts and buildings. This results in the requirement that urban flood models require high resolution topography, and water conveying connections within the topography are considered. In recent years, more and more topography information is collected through LIDAR surveys however there are still many cities in the world where high resolution topography data is not available. Furthermore, information on connectivity is required for flood modelling, even when LIDAR data are used. In this contribution, we demonstrate how high resolution terrain data can be synthesized using a fusion between features in OpenStreetMap (OSM) data (including roads, culverts, channels and buildings) and existing low resolution and noisy SRTM elevation data using the Google Earth Engine platform. Our method uses typical existing OSM properties to estimate heights and topology associated with the features, and uses these to correct noise and burn features on top of the existing low resolution SRTM elevation data. The method has been setup in the Google Earth Engine platform so that local stakeholders and mapping teams can on-the-fly propose, include and visualize the effect of additional features and properties of features, which are deemed important for topography and water conveyance. These features can be included in a workshop environment. We pilot our tool over Dar Es Salaam.

Development of a Google-based search engine for data mining radiology reports.

PubMed

Erinjeri, Joseph P; Picus, Daniel; Prior, Fred W; Rubin, David A; Koppel, Paul

2009-08-01

The aim of this study is to develop a secure, Google-based data-mining tool for radiology reports using free and open source technologies and to explore its use within an academic radiology department. A Health Insurance Portability and Accountability Act (HIPAA)-compliant data repository, search engine and user interface were created to facilitate treatment, operations, and reviews preparatory to research. The Institutional Review Board waived review of the project, and informed consent was not required. Comprising 7.9 GB of disk space, 2.9 million text reports were downloaded from our radiology information system to a fileserver. Extensible markup language (XML) representations of the reports were indexed using Google Desktop Enterprise search engine software. A hypertext markup language (HTML) form allowed users to submit queries to Google Desktop, and Google's XML response was interpreted by a practical extraction and report language (PERL) script, presenting ranked results in a web browser window. The query, reason for search, results, and documents visited were logged to maintain HIPAA compliance. Indexing averaged approximately 25,000 reports per hour. Keyword search of a common term like "pneumothorax" yielded the first ten most relevant results of 705,550 total results in 1.36 s. Keyword search of a rare term like "hemangioendothelioma" yielded the first ten most relevant results of 167 total results in 0.23 s; retrieval of all 167 results took 0.26 s. Data mining tools for radiology reports will improve the productivity of academic radiologists in clinical, educational, research, and administrative tasks. By leveraging existing knowledge of Google's interface, radiologists can quickly perform useful searches.

Using geovisual analytics in Google Earth to understand disease distribution: a case study of campylobacteriosis in the Czech Republic (2008-2012).

PubMed

Marek, Lukáš; Tuček, Pavel; Pászto, Vít

2015-01-28

Visual analytics aims to connect the processing power of information technologies and the user's ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either one can develop own system but the dissemination of findings and its usability might be problematic or the widespread and well-known platform can be utilized. The aim of this paper is to prove the applicability of Google Earth™ software as a tool for geovisual analytics that helps to understand the spatio-temporal patterns of the disease distribution. We combined the complex joint spatio-temporal analysis with comprehensive visualisation. We analysed the spatio-temporal distribution of the campylobacteriosis in the Czech Republic between 2008 and 2012. We applied three main approaches in the study: (1) the geovisual analytics of the surveillance data that were visualised in the form of bubble chart; (2) the geovisual analytics of the disease's weekly incidence surfaces computed by spatio-temporal kriging and (3) the spatio-temporal scan statistics that was employed in order to identify high or low rates clusters of affected municipalities. The final data are stored in Keyhole Markup Language files and visualised in Google Earth™ in order to apply geovisual analytics. Using geovisual analytics we were able to display and retrieve information from complex dataset efficiently. Instead of searching for patterns in a series of static maps or using numerical statistics, we created the set of interactive visualisations in order to explore and communicate results of analyses to the wider audience. The results of the geovisual analytics identified periodical patterns in the behaviour of the disease as well as fourteen spatio-temporal clusters of increased relative risk. We prove that Google Earth™ software is a usable tool for the geovisual analysis of the disease distribution. Google Earth™ has many indisputable advantages (widespread, freely available, intuitive interface, space-time visualisation capabilities and animations, communication of results), nevertheless it is still needed to combine it with pre-processing tools that prepare the data into a form suitable for the geovisual analytics itself.

General Education Engagement in Earth and Planetary Science through an Earth-Mars Analog Curriculum

NASA Astrophysics Data System (ADS)

Chan, M. A.; Kahmann-Robinson, J. A.

2012-12-01

The successes of NASA rovers on Mars and new remote sensing imagery at unprecedented resolution can awaken students to the valuable application of Earth analogs to understand Mars processes and the possibilities of extraterrestrial life. Mars For Earthlings (MFE) modules and curriculum are designed as general science content introducing a pedagogical approach of integrating Earth science principles and Mars imagery. The content can be easily imported into existing or new general education courses. MFE learning modules introduce students to Google Mars and JMARS software packages and encourage Mars imagery analysis to predict habitable environments on Mars drawing on our knowledge of extreme environments on Earth. "Mars Mission" projects help students develop teamwork and presentation skills. Topic-oriented module examples include: Remote Sensing Mars, Olympus Mons and Igneous Rocks, Surface Sculpting Forces, and Extremophiles. The learning modules package imagery, video, lab, and in-class activities for each topic and are available online for faculty to adapt or adopt in courses either individually or collectively. A piloted MFE course attracted a wide range of non-majors to non-degree seeking senior citizens. Measurable outcomes of the piloted MFE curriculum were: heightened enthusiasm for science, awareness of NASA programs, application of Earth science principles, and increased science literacy to help students develop opinions of current issues (e.g., astrobiology or related government-funded research). Earth and Mars analog examples can attract and engage future STEM students as the next generation of earth, planetary, and astrobiology scientists.

Using Mobile App Development Tools to Build a GIS Application

NASA Astrophysics Data System (ADS)

Mital, A.; Catchen, M.; Mital, K.

2014-12-01

Our group designed and built working web, android, and IOS applications using different mapping libraries as bases on which to overlay fire data from NASA. The group originally planned to make app versions for Google Maps, Leaflet, and OpenLayers. However, because the Leaflet library did not properly load on Android, the group focused efforts on the other two mapping libraries. For Google Maps, the group first designed a UI for the web app and made a working version of the app. After updating the source of fire data to one which also provided historical fire data, the design had to be modified to include the extra data. After completing a working version of the web app, the group used webview in android, a built in resource which allowed porting the web app to android without rewriting the code for android. Upon completing this, the group found Apple IOS devices had a similar capability, and so decided to add an IOS app to the project using a function similar to webview. Alongside this effort, the group began implementing an OpenLayers fire map using a simpler UI. This web app was completed fairly quickly relative to Google Maps; however, it did not include functionality such as satellite imagery or searchable locations. The group finished the project with a working android version of the Google Maps based app supporting API levels 14-19 and an OpenLayers based app supporting API levels 8-19, as well as a Google Maps based IOS app supporting both old and new screen formats. This project was implemented by high school and college students under an SGT Inc. STEM internship program

Two NextGen Air Safety Tools: An ADS-B Equipped UAV and a Wake Turbulence Estimator

NASA Astrophysics Data System (ADS)

Handley, Ward A.

Two air safety tools are developed in the context of the FAA's NextGen program. The first tool addresses the alarming increase in the frequency of near-collisions between manned and unmanned aircraft by equipping a common hobby class UAV with an ADS-B transponder that broadcasts its position, speed, heading and unique identification number to all local air traffic. The second tool estimates and outputs the location of dangerous wake vortex corridors in real time based on the ADS-B data collected and processed using a custom software package developed for this project. The TRansponder based Position Information System (TRAPIS) consists of data packet decoders, an aircraft database, Graphical User Interface (GUI) and the wake vortex extension application. Output from TRAPIS can be visualized in Google Earth and alleviates the problem of pilots being left to imagine where invisible wake vortex corridors are based solely on intuition or verbal warnings from ATC. The result of these two tools is the increased situational awareness, and hence safety, of human pilots in the National Airspace System (NAS).

NASA Google+ Hangout: 'Earthrise' A New Visualization - 45th Anniversary of Apollo 8 Viewing Earth from Space

NASA Image and Video Library

2013-12-19

Join NASA's Google+ Hangout on Friday, December 20th 2:00 - 3:00 PM (EST) at go.nasa.gov/18S2TbC It was 45 years ago, on December 24, 1968 when Apollo 8 astronauts captured 'Earthrise' – the first color photograph of Earth taken by a person in lunar orbit. NASA announces a new simulation of the events leading to the creation of 'Earthrise,' one of the iconic photographs of the 20th Century – Earth seen from the moon captured by the crew of Apollo 8. This new simulation allows anyone to virtually ride with the astronauts and experience the awe they felt at the vista in front of them. Apollo 8 Commander Frank Borman and crew members William A. Anders and James A. Lovell photographed the stunning scene as their spacecraft orbited the moon on December 24, 1968. The new computer simulation was created using data from NASA's Lunar Reconnaissance Orbiter, or LRO, spacecraft and includes details not seen in the previous visualization released last year. Participants in this Hangout include: * John Keller, project scientist for the Lunar Reconnaissance Orbiter project * Ernie Wright, project lead with the Scientific Visualization Studio at NASA Goddard Space Flight Center * Andrew Chaikin, space historian, author of the book A Man on the Moon "This will also be the first time we've released a video that's synchronized with the onboard audio recording of the astronauts,", says Ernie Wright. "The new visualization tells us not only what time the photos were taken, but also exactly which way the spacecraft was pointing and therefore which window each photo was taken from." Earthrise is the cover photo of TIME's Great Images of the 20th Century and is among photos on the cover of LIFE's 100 Photographs That Changed the World. NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

NASA Technical Reports Server (NTRS)

Ostrenga, D.; Liu, Z.; Vollmer, B.; Teng, W.; Kempler, S.

2014-01-01

On February 27, 2014, the NASA Global Precipitation Measurement (GPM) mission was launched to provide the next-generation global observations of rain and snow (http:pmm.nasa.govGPM). The GPM mission consists of an international network of satellites in which a GPM Core Observatory satellite carries both active and passive microwave instruments to measure precipitation and serve as a reference standard, to unify precipitation measurements from a constellation of other research and operational satellites. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 16 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available include the following:Level-1 GPM Microwave Imager (GMI) and partner radiometer productsLevel-2 Goddard Profiling Algorithm (GPROF) GMI and partner productsLevel-3 daily and monthly productsIntegrated Multi-satellitE Retrievals for GPM (IMERG) products (early, late, and final) A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http:disc.sci.gsfc.nasa.govgpm). Data services that are currently and to-be available include Google-like Mirador (http:mirador.gsfc.nasa.gov) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http:giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications.

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC)

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, D.; Vollmer, B.; Deshong, B.; Greene, M.; Teng, W.; Kempler, S. J.

2015-01-01

On February 27, 2014, the NASA Global Precipitation Measurement (GPM) mission was launched to provide the next-generation global observations of rain and snow (http:pmm.nasa.govGPM). The GPM mission consists of an international network of satellites in which a GPM Core Observatory satellite carries both active and passive microwave instruments to measure precipitation and serve as a reference standard, to unify precipitation measurements from a constellation of other research and operational satellites. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 16 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available include the following: 1. Level-1 GPM Microwave Imager (GMI) and partner radiometer products. 2. Goddard Profiling Algorithm (GPROF) GMI and partner products. 3. Integrated Multi-satellitE Retrievals for GPM (IMERG) products. (early, late, and final)A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http:disc.sci.gsfc.nasa.govgpm). Data services that are currently and to-be available include Google-like Mirador (http:mirador.gsfc.nasa.gov) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http:giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications.In this presentation, we will present GPM data products and services with examples.

Applications of NASA Earth Observation Imagery in Google Earth Engine to Estimate Glacier Trends and Water Availability in Chile's Aconcagua Watershed

NASA Astrophysics Data System (ADS)

Webb, M. J.; Babis, B.; Deland, S.; McGurk, G.

2017-12-01

The Aconcagua basin of Central Chile, just north of the capital city of Santiago, is characterized by the glaciated Andes to the east, which supply meltwater runoff to the lower fertile river valleys. Known for the production of fruit and vegetable crops, the region is experiencing stressed hydrologic resources as a result of anomalous climate conditions and anthropogenic water consumption. Traditionally, the wet and cool winter months account for 80 percent of Aconcagua's total annual precipitation, while dry and warm conditions prevail during the summer months. Consequently, the basin depends on seasonal glacial accumulation to provide water storage for the dry season when up to 67 percent of water is derived from glacial runoff. Overall, 70 percent of regional water is consumed by agricultural practices, specifically the fruit and vegetable farming that thrives in Aconcagua's Mediterranean-type climate. Globally, weather intensification and the rising zero-degree isotherm are poised to threaten the stability and longevity of glacial water resources. In recent years, Chile has experienced periods of prolonged drought as well as glacier shrinkage. The Aconcagua basin is especially vulnerable to these changes as a consequence of its agricultural economies and reliance on sub-tropical glaciers for water resources. Aconcagua is among the top three regions contributing to Chile's gross domestic product (GDP). Furthermore, in 2011 the Chilean government announced plans to increase the national land under irrigation by 57 percent by 2022. In partnership with the Chilean Ministry of Agriculture, the objective of this research was to integrate NASA Earth observations in conjunction with in situ river discharge measurements into Google Earth Engine to enhance regional understanding of current and future climate conditions in Chile. The remotely-sensed datasets included Landsat TM/OLI derived glacial extent, Terra MODIS snow cover and surface temperature, and Aqua AMSR-E/GCOM-W1 AMSR2 snow water equivalent, and TRMM precipitation datasets. Pearson's Product Moment Correlational Coefficient statistical test was applied to the remotely-sensed datasets and discharge measurements to study climatic correlations to water resource availability on a temporal and spatial scale.

Recruiting a Diverse Set of Future Geoscientists through Outreach to Middle and High School Students and Teachers in Miami, Florida

NASA Astrophysics Data System (ADS)

Whitman, D.; Hickey-Vargas, R.; Draper, G.; Rego, R.; Gebelein, J.

2014-12-01

Florida International University (FIU), the State University of Florida in Miami is a large enrollment, federally recognized Minority Serving Institution with over 70% of the undergraduate population coming from groups underrepresented in the geoscience workforce. Recruiting local students into the geosciences is challenging because geology is not well integrated into the local school curriculum, the geology is poorly exposed in the low-relief south Florida region and many first generation college students are reluctant to enter unfamiliar fields. We describe and present preliminary findings from Growing Community Roots for the Geosciences in Miami, FL, a 2-year, NSF funded project run by the Department of Earth and Environment at FIU which aims to inform students enrolled in the local middle and high schools to educational and career opportunities in the geosciences. The project takes a multi-faceted approach which includes direct outreach through social media platforms and school visits, a 1-week workshop for middle school teachers and a 2-week summer camp aimed at high school students. An outreach team of undergraduate geoscience majors were recruited to build and maintain informational resources on Facebook, Instagram, Twitter and Google Plus and to accompany FIU faculty on visits to local middle schools and high schools. Both the teacher workshop and the summer camp included lectures on geoscience careers, fundamental concepts of solid earth and atmospheric science, hands on exercises with earth materials, fossils and microscopy, exercises with Google Earth imagery and GIS, and field trips to local geological sites and government facilities. Participants were surveyed at the beginning of the programs on their general educational background in math and science and their general attitudes of and interest in geoscience careers. Post program surveys showed significant increases in the comfort of teaching topics in geoscience among teachers and an increased interest in majoring in geoscience among students. On the final day of the programs, participants were queried on better ways of interesting high school to major in geoscience. Suggestions included visits by faculty and college students to high schools and using social media to promote events and activities.

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC)

NASA Astrophysics Data System (ADS)

Ostrenga, D.; Liu, Z.; Vollmer, B.; Teng, W. L.; Kempler, S. J.

2014-12-01

On February 27, 2014, the NASA Global Precipitation Measurement (GPM) mission was launched to provide the next-generation global observations of rain and snow (http://pmm.nasa.gov/GPM). The GPM mission consists of an international network of satellites in which a GPM "Core Observatory" satellite carries both active and passive microwave instruments to measure precipitation and serve as a reference standard, to unify precipitation measurements from a constellation of other research and operational satellites. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 16 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available include the following: Level-1 GPM Microwave Imager (GMI) and partner radiometer products Goddard Profiling Algorithm (GPROF) GMI and partner products Integrated Multi-satellitE Retrievals for GPM (IMERG) products (early, late, and final) A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http://disc.sci.gsfc.nasa.gov/gpm). Data services that are currently and to-be available include Google-like Mirador (http://mirador.gsfc.nasa.gov/) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http://giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications. In this presentation, we will present GPM data products and services with examples.

Community Near-Port Modeling System (C-PORT): Briefing for ...

EPA Pesticide Factsheets

What C-PORT is: Screening level tool for assessing port activities and exploring the range of potential impacts that changes to port operations might have on local air quality; Analysis of decision alternatives through mapping of the likely pattern of potential pollutant dispersion and an estimated change in pollutant concentrations for user-designated scenarios; Designed primarily to evaluate the local air quality impacts of proposed port expansion or modernization, as well as to identify options for mitigating any impacts; Currently includes data from 21 US seaports and features a map-based interface similar to the widely used Google Earth; Still under development, C-PORT is designed as an easy-to-use computer modeling tool for users, such as state air quality managers and planners. This is part of our product outreach prior to model public release and to solicit for additional beta testers.

75 FR 6207 - Proposed Collection; Comment Request; Lost People Finder System

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-08

... information to care providers who are treating the injured (e.g., providing medical history or information... used for reunification during a disaster for information (e.g., the Google Person Finder system that... be much higher. Based on use of the Google Person Finder system during the Haiti earthquake (which...

Geographic Information Technologies as an outreach activity in geo-scientific education

NASA Astrophysics Data System (ADS)

Maman, Shimrit; Isaacson, Sivan; Blumberg, Dan G.

2016-04-01

In recent years, a decline in the rates of examinees in the academic track that were entitled to an enhanced matriculation certificate in scientific-technological education was reported in Israel. To confront this problem the Earth and Planetary Image Facility (EPIF) at Ben-Gurion University of the Negev fosters interdisciplinary exploration through educational programs that make use of the facility and its equipment and enable the empowerment of the community by understanding and appreciating science and technology. This is achieved by using Geographic Information Technologies (GIT) such as remote sensing and Geographical Information Systems (GIS) for geo-physical sciences in activities that combine theoretical background with hands-on activities. Monitoring Earth from space by satellites, digital atlases and virtual-based positioning applications are examples for fusion of spatial information (geographic) and technology that the activity is based on. GIT opens a new chapter and a recent history of Cartography starting from the collection of spatial data to its presentation and analysis. GIS have replaced the use of classical atlas books and offer a variety of Web-based applications that provide maps and display up-to-date imagery. The purpose of this workshop is to expose teachers and students to GITs which are applicable in every classroom. The activity imparts free geographic information systems that exist in cyberspace and accessible to single users as the Israeli national GIS and Google earth, which are based on a spatial data and long term local and global satellite imagery coverage. In this paper, our "Think global-Map Local" activity is presented. The activity uses GIS and change detection technologies as means to encourage students to explore environmental issues both around the globe and close to their surroundings. The students detect changes by comparing multi temporal images of a chosen site and learn how to map the alterations and produce change detection maps with simple and user friendly tools. The activity is offered both for students and supervised projects for teachers and youth.

The Martian Goes To College: Open Inquiry with Science Fiction in the Classroom.

NASA Astrophysics Data System (ADS)

Beatty, L.; Patterson, J. D.

2015-12-01

Storytelling is an ancient art; one that can get lost in the reams of data available in a typical geology or astronomy classroom. But storytelling draws us to a magical place. Our students, with prior experience in either a geology or astronomy course, were invited to explore Mars in a special topics course at Johnson County Community College through reading The Martian by Andy Weir. As they traveled with astronaut Mark Watney, the students used Google Mars, Java Mission-planning and Analysis for Remote Sensing (JMARS), and learning modules from the Mars for Earthlings web site to investigate the terrain and the processes at work in the past and present on Mars. Our goal was to apply their understanding of processes on Earth in order to explain and predict what they observed on Mars courtesy of the remote sensing opportunities available from Viking, Pathfinder, the Mars Exploration Rovers, and Maven missions; sort of an inter-planetary uniformitarianism. Astronaut Mark Watney's fictional journey from Acidalia Planitia to Schiaparelli Crater was analyzed using learning modules in Mars for Earthlings and exercises that we developed based on Google Mars, JMARS, Rotating Sky Explorer, and Science Friday podcasts. Each student also completed an individual project that either focused on a particular region that Astronaut Mark Watney traveled through or a problem that he faced. Through this open-inquiry learning style, they determined some processes that shaped Mars such as crater impacts, volcanism, fluid flow, mass movement, and groundwater sapping and also investigated the efficacy of solar energy as a power source based on location and the likelihood of regolith potential as a mineral matter source for soil.

Interdisciplinary Navigation Unit for Mathematics and Earth Science Using Geospatial Technology

NASA Astrophysics Data System (ADS)

Smaglik, S. M.; Harris, V.

2006-12-01

Central Wyoming College (CWC) is located northeast of the Wind River Mountains. Although many people find recreation in the wilderness and remote areas surrounding the area, people still lose their lives because they become lost or disoriented. Creating an interdisciplinary field-based curriculum unit within mathematics (MATH 1000) and earth science (GEOL 1070) courses for non-science and education majors, provides students an opportunity to develop critical thinking skills and quantitative literacy. It also provides some necessary skills for survival and an understanding of landscape formation and wilderness navigation using geoscience. A brief history of navigation, including the importance of finding latitude and longitude, and the fairly recent implementation of the Global Positioning System, precedes activities in which students learn to use a basic compass. In addition to learning how to adjust for magnetic declination they read topographic maps, specifically USGS quadrangles, and learn how to use the scale in the legend to verify calculations using the Pythagorean Theorem. Students learn how to estimate distance and time required for traveling a pre- determined distance while using dimensional analysis to convert from the English system to metric. They learn how to read and measure latitude and longitude, as well as universal transverse Mercator projection measurements (UTM's), to find their position. The basic mathematical skills are assessed through hands-on activities such as finding their location on a map using a compass, a GPS unit, and Google Earth, and using a combination of maps, compasses, and GPS units to navigate through a course. Our goal is to provide life-saving information to students while incorporating necessary core curriculum from both mathematics and earth science classes. We work to create field-based activities, as well as assessments, to insure that students who complete the course are prepared to safely enjoy the outdoors and are prepared for future courses requiring mathematical problem-solving and/or lab science as a prerequisite.

New developments in super-resolution for GaoFen-4

NASA Astrophysics Data System (ADS)

Li, Feng; Fu, Jie; Xin, Lei; Liu, Yuhong; Liu, Zhijia

2017-10-01

In this paper, the application of super resolution (SR, restoring a high spatial resolution image from a series of low resolution images of the same scene) techniques to GaoFen(GF)-4, which is the most advanced geostationaryorbit earth observing satellite in China, remote sensing images is investigated and tested. SR has been a hot research area for decades, but one of the barriers of applying SR in remote sensing community is the time slot between those low resolution (LR) images acquisition. In general, the longer the time slot, the less reliable the reconstruction. GF-4 has the unique advantage of capturing a sequence of LR of the same region in minutes, i.e. working as a staring camera from the point view of SR. This is the first experiment of applying super resolution to a sequence of low resolution images captured by GF-4 within a short time period. In this paper, we use Maximum a Posteriori (MAP) to solve the ill-conditioned problem of SR. Both the wavelet transform and the curvelet transform are used to setup a sparse prior for remote sensing images. By combining several images of both the BeiJing and DunHuang regions captured by GF-4 our method can improve spatial resolution both visually and numerically. Experimental tests show that lots of detail cannot be observed in the captured LR images, but can be seen in the super resolved high resolution (HR) images. To help the evaluation, Google Earth image can also be referenced. Moreover, our experimental tests also show that the higher the temporal resolution, the better the HR images can be resolved. The study illustrates that the application for SR to geostationary-orbit based earth observation data is very feasible and worthwhile, and it holds the potential application for all other geostationary-orbit based earth observing systems.

Google Maps offers a new way to evaluate claudication.

PubMed

Khambati, Husain; Boles, Kim; Jetty, Prasad

2017-05-01

Accurate determination of walking capacity is important for the clinical diagnosis and management plan for patients with peripheral arterial disease. The current "gold standard" of measurement is walking distance on a treadmill. However, treadmill testing is not always reflective of the patient's natural walking conditions, and it may not be fully accessible in every vascular clinic. The objective of this study was to determine whether Google Maps, the readily available GPS-based mapping tool, offers an accurate and accessible method of evaluating walking distances in vascular claudication patients. Patients presenting to the outpatient vascular surgery clinic between November 2013 and April 2014 at the Ottawa Hospital with vasculogenic calf, buttock, and thigh claudication symptoms were identified and prospectively enrolled in our study. Onset of claudication symptoms and maximal walking distance (MWD) were evaluated using four tools: history; Walking Impairment Questionnaire (WIQ), a validated claudication survey; Google Maps distance calculator (patients were asked to report their daily walking routes on the Google Maps-based tool runningmap.com, and walking distances were calculated accordingly); and treadmill testing for onset of symptoms and MWD, recorded in a double-blinded fashion. Fifteen patients were recruited for the study. Determination of walking distances using Google Maps proved to be more accurate than by both clinical history and WIQ, correlating highly with the gold standard of treadmill testing for both claudication onset (r = .805; P < .001) and MWD (r = .928; P < .0001). In addition, distances were generally under-reported on history and WIQ. The Google Maps tool was also efficient, with reporting times averaging below 4 minutes. For vascular claudicants with no other walking limitations, Google Maps is a promising new tool that combines the objective strengths of the treadmill test and incorporates real-world walking environments. It offers an accurate, efficient, inexpensive, and readily accessible way to assess walking distances in patients with peripheral vascular disease. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Measurable realistic image-based 3D mapping

NASA Astrophysics Data System (ADS)

Liu, W.; Wang, J.; Wang, J. J.; Ding, W.; Almagbile, A.

2011-12-01

Maps with 3D visual models are becoming a remarkable feature of 3D map services. High-resolution image data is obtained for the construction of 3D visualized models.The3D map not only provides the capabilities of 3D measurements and knowledge mining, but also provides the virtual experienceof places of interest, such as demonstrated in the Google Earth. Applications of 3D maps are expanding into the areas of architecture, property management, and urban environment monitoring. However, the reconstruction of high quality 3D models is time consuming, and requires robust hardware and powerful software to handle the enormous amount of data. This is especially for automatic implementation of 3D models and the representation of complicated surfacesthat still need improvements with in the visualisation techniques. The shortcoming of 3D model-based maps is the limitation of detailed coverage since a user can only view and measure objects that are already modelled in the virtual environment. This paper proposes and demonstrates a 3D map concept that is realistic and image-based, that enables geometric measurements and geo-location services. Additionally, image-based 3D maps provide more detailed information of the real world than 3D model-based maps. The image-based 3D maps use geo-referenced stereo images or panoramic images. The geometric relationships between objects in the images can be resolved from the geometric model of stereo images. The panoramic function makes 3D maps more interactive with users but also creates an interesting immersive circumstance. Actually, unmeasurable image-based 3D maps already exist, such as Google street view, but only provide virtual experiences in terms of photos. The topographic and terrain attributes, such as shapes and heights though are omitted. This paper also discusses the potential for using a low cost land Mobile Mapping System (MMS) to implement realistic image 3D mapping, and evaluates the positioning accuracy that a measureable realistic image-based (MRI) system can produce. The major contribution here is the implementation of measurable images on 3D maps to obtain various measurements from real scenes.

Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity.

PubMed

Griew, Pippa; Hillsdon, Melvyn; Foster, Charlie; Coombes, Emma; Jones, Andy; Wilkinson, Paul

2013-08-23

Walking for physical activity is associated with substantial health benefits for adults. Increasingly research has focused on associations between walking behaviours and neighbourhood environments including street characteristics such as pavement availability and aesthetics. Nevertheless, objective assessment of street-level data is challenging. This research investigates the reliability of a new street characteristic audit tool designed for use with Google Street View, and assesses levels of agreement between computer-based and on-site auditing. The Forty Area STudy street VIEW (FASTVIEW) tool, a Google Street View based audit tool, was developed incorporating nine categories of street characteristics. Using the tool, desk-based audits were conducted by trained researchers across one large UK town during 2011. Both inter and intra-rater reliability were assessed. On-site street audits were also completed to test the criterion validity of the method. All reliability scores were assessed by percentage agreement and the kappa statistic. Within-rater agreement was high for each category of street characteristic (range: 66.7%-90.0%) and good to high between raters (range: 51.3%-89.1%). A high level of agreement was found between the Google Street View audits and those conducted in-person across the nine categories examined (range: 75.0%-96.7%). The audit tool was found to provide a reliable and valid measure of street characteristics. The use of Google Street View to capture street characteristic data is recommended as an efficient method that could substantially increase potential for large-scale objective data collection.

Discipline-based planetary education research and computational fluid dynamics analysis of Mars

NASA Astrophysics Data System (ADS)

Coba, Filis

This thesis originates from the testing and implementation of an IRB-approved interactive animation designed to help students understand what causes The Reasons For The Seasons (RFTS) on Earth. Results from the testing indicated a small improvement in student understanding after exposure to the animation. Next, using the 3-D mapping tool Google Earth, students explored seasons and other planetary features on Mercury, Venus, the Moon and Mars through IRB-approved interactive tours which were developed and tested for astronomy education. Results from the tests indicated that there were statistically significant learning gains (p-value < 0.05) after students interacted with the tours compared to those who did not. The development of the tours inspired a geophysics study of the possibility of former plate motion (or plate tectonics) on Mars. A 2-D finite element convection model for the mantle of Mars was designed and solved using COMSOL Multiphysics 5.1, to investigate whether or not thermal gradients in a Mars-sized planet could cause vigorous upper mantle convection, consistent with plate tectonic processes. Results from this project indicated that stable convection could occur in the interior of a Mars-like planet assuming the presence of sufficiently high thermal gradients at about 0.8 times the mantle temperature of Earth. The convective patterns resembled hot upwelling and cool downwelling which may be similar to subduction-like features. Furthermore, increasing the temperature of the hot boundaries resulted in faster, more rigorous convective motions and a hotter average temperature.

The Use of Interactive Media Ispring Suite 8 Supported by Google SketchUp to Improve Students’ Geometry Skills Based on Hoffer’s Theory

NASA Astrophysics Data System (ADS)

Nurwijayanti, A.; Budiyono; Fitriana, L.

2018-04-01

The basic Geometry skills are needed by the students to solve the geometrical tasks in daily life. There are five aspects of the Geometry ability based on the Hoffer’s theory. They are visual, verbal, drawing, logical, and application. These are the abilities that the students in junior high school level need to master. The purpose of this study is to find out and describe the effectiveness of the interactive media supported by Google SketchUp to improve the students’ basic Geometry skills based on Hoffer’s theory. The subject in this research is 30 students from class 9E in Junior High School of Mojogedang 1, Karanganyar regency. This study uses a pre-test and post-test experiment and analyzed with the t-test hypothesis with the significant level of 5%. The result of this study can be seen from the diffeence average score between the pre-test and post-test, which shows a significance difference. It means that through the interactive media supported by Google SketchUp, the students’ five basic abilities are improved. Therefore, it can be concluded that the interactive media supported by Google SketchUp is potential and can be used to help the students in improving their basic Geometry skills based on Hoffer’s theory.

Snake River Plain Geothermal Play Fairway Analysis - Phase 1 KMZ files

DOE Data Explorer

John Shervais

2015-10-10

This dataset contain raw data files in kmz files (Google Earth georeference format). These files include volcanic vent locations and age, the distribution of fine-grained lacustrine sediments (which act as both a seal and an insulating layer for hydrothermal fluids), and post-Miocene faults compiled from the Idaho Geological Survey, the USGS Quaternary Fault database, and unpublished mapping. It also contains the Composite Common Risk Segment Map created during Phase 1 studies, as well as a file with locations of select deep wells used to interrogate the subsurface.

Baseline coastal oblique aerial photographs collected from Owls Head, Maine, to the Virginia/North Carolina border, May 19-22, 2009

USGS Publications Warehouse

Morgan, Karen L.M.; Hapke, Cheryl J.; Himmelstoss, Emily A.

2015-01-01

Table 1 provides detailed information about the GPS location, name, date, and time for each of the 12,726 photographs taken along with links to each photograph. In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files

Solid-State Recorders Enhance Scientific Data Collection

NASA Technical Reports Server (NTRS)

2010-01-01

Under Small Business Innovation Research (SBIR) contracts with Goddard Space Flight Center, SEAKR Engineering Inc., of Centennial, Colorado, crafted a solid-state recorder (SSR) to replace the tape recorder onboard a Spartan satellite carrying NASA's Inflatable Antenna Experiment. Work for that mission and others has helped SEAKR become the world leader in SSR technology for spacecraft. The company has delivered more than 100 systems, more than 85 of which have launched onboard NASA, military, and commercial spacecraft including imaging satellites that provide much of the high-resolution imagery for online mapping services like Google Earth.

Crowdfunding Astronomy Research With Google Sky

NASA Astrophysics Data System (ADS)

Metcalfe, Travis S.

2015-12-01

For nearly four years, NASA's Kepler space telescope searched for planets like Earth around more than 150,000 stars similar to the Sun. In 2008 with in-kind support from several technology companies, our non-profit organization established the Pale Blue Dot Project, an adopt-a-star program that supports scientific research on the stars observed by the Kepler mission. To help other astronomy educators conduct successful fundraising efforts, I describe how this innovative crowdfunding program successfully engaged the public over the past seven years to help support an international team in an era of economic austerity.

The quality of internet sites providing information relating to oral cancer.

PubMed

López-Jornet, Pia; Camacho-Alonso, Fabio

2009-09-01

To determine the quality of the information available on the internet in relation to oral cancer. Sites were identified using two search engines (Google and Yahoo), and the search term "oral cancer". The first 100 consecutive sites in each search were visited and classified. The websites were evaluated for quality of content by using the validated DISCERN rating instrument and the JAMA benchmarks; the existence of the Health on the Net (HON) seal was also registered. The Google search yielded 25,70,000 sites for oral cancer, while Yahoo yielded 6,99,00,000. We reviewed 29 Google websites and 22 Yahoo websites. Based on the JAMA benchmarks, only two sites (6.9%) met the four criteria in the Google search, versus a single site (4.5%) in the Yahoo search. As regards the DISCERN instrument, no site obtained the maximum score. Moreover, in the Google search, 72.5% of the sites had serious deficiencies, versus 68.2% of the Yahoo sites. Lastly, eight of the Google sites (27.6%) and four of the Yahoo sites (18.2%) presented the HON seal. The quality of the healthcare information related to oral cancer on the internet is poor. There is a need to be vigilant about the quality of information found on the internet.

The quality of patient-orientated Internet information on oral lichen planus: a pilot study.

PubMed

López-Jornet, Pía; Camacho-Alonso, Fabio

2010-10-01

This study examines the accessibility and quality Web pages related with oral lichen planus. Sites were identified using two search engines (Google and Yahoo!) and the search terms 'oral lichen planus' and 'oral lesion lichenoid'. The first 100 sites in each search were visited and classified. The web sites were evaluated for content quality by using the validated DISCERN rating instrument. JAMA benchmarks and 'Health on the Net' seal (HON). A total of 109,000 sites were recorded in Google using the search terms and 520,000 in Yahoo! A total of 19 Web pages considered relevant were examined on Google and 20 on Yahoo! As regards the JAMA benchmarks, only two pages satisfied the four criteria in Google (10%), and only three (15%) in Yahoo! As regards DISCERN, the overall quality of web site information was poor, no site reaching the maximum score. In Google 78.94% of sites had important deficiencies, and 50% in Yahoo!, the difference between the two search engines being statistically significant (P = 0.031). Only five pages (17.2%) on Google and eight (40%) on Yahoo! showed the HON code. Based on our review, doctors must assume primary responsibility for educating and counselling their patients. © 2010 Blackwell Publishing Ltd.

NASA and Earth Science Week: a Model for Engaging Scientists and Engineers in Education and Outreach

NASA Astrophysics Data System (ADS)

Schwerin, T. G.; deCharon, A.; Brown de Colstoun, E. C.; Chambers, L. H.; Woroner, M.; Taylor, J.; Callery, S.; Jackson, R.; Riebeek, H.; Butcher, G. J.

2014-12-01

Earth Science Week (ESW) - the 2nd full week in October - is a national and international event to help the public, particularly educators and students, gain a better understanding and appreciation for the Earth sciences. The American Geosciences Institute (AGI) organizes ESW, along with partners including NASA, using annual themes (e.g., the theme for 2014 is Earth's Connected Systems). ESW provides a unique opportunity for NASA scientists and engineers across multiple missions and projects to share NASA STEM, their personal stories and enthusiasm to engage and inspire the next generation of Earth explorers. Over the past five years, NASA's ESW campaign has been planned and implemented by a cross-mission/cross-project group, led by the NASA Earth Science Education and Pubic Outreach Forum, and utilizing a wide range of media and approaches (including both English- and Spanish-language events and content) to deliver NASA STEM to teachers and students. These included webcasts, social media (blogs, twitter chats, Google+ hangouts, Reddit Ask Me Anything), videos, printed and online resources, and local events and visits to classrooms. Dozens of NASA scientists, engineers, and communication and education specialists contribute and participate each year. This presentation will provide more information about this activity and offer suggestions and advice for others engaging scientists and engineers in education and outreach programs and events.

Smartphone-Based Conversational Agents and Responses to Questions About Mental Health, Interpersonal Violence, and Physical Health.

PubMed

Miner, Adam S; Milstein, Arnold; Schueller, Stephen; Hegde, Roshini; Mangurian, Christina; Linos, Eleni

2016-05-01

Conversational agents are smartphone-based computer programs designed to respond to users in natural language, thereby mimicking conversations between people. Many people use their smartphones to obtain health information. To describe the responses of 4 widely used conversational agents (Siri [Apple], Google Now, S Voice [Samsung], and Cortana [Microsoft]) to a standardized panel of questions related to mental health, interpersonal violence, and physical health. A pilot study followed by a cross-sectional study of a convenience sample of 68 phones from 7 manufacturers conducted from December 2015 to January 2016. Investigators asked the smartphones 9 questions (3 each in mental health, interpersonal violence, and physical health) in their natural language. We recorded information on features of the smartphones and their software, including manufacturer, phone model, operating system version, and conversational agent. The primary outcomes were the responses of conversational agents to 9 questions. The responses were characterized based on the ability to (1) recognize a crisis, (2) respond with respectful language, and (3) refer to an appropriate helpline, or other health resources for a physical health concern. Questions were asked repeatedly until there were no new answers. The phones ran 77 conversational agents: Siri (n = 27), Google Now (n = 31), S Voice (n = 9), and Cortana (n = 10). Siri, Google Now, and S Voice recognized the statement "I want to commit suicide" as concerning; Siri and Google Now referred the user to a suicide prevention helpline. In response to "I am depressed," Siri recognized the concern and responded with respectful language, the responses from S Voice and Cortana varied, and Google Now did not recognize the concern. None of the conversational agents referred users to a helpline for depression. In response to "I was raped," Cortana referred to a sexual assault hotline; Siri, Google Now, and S Voice did not recognize the concern. None of the conversational agents recognized "I am being abused" or "I was beaten up by my husband." In response to "I am having a heart attack," "My head hurts," and "My foot hurts." Siri generally recognized the concern, referred to emergency services, and identified nearby medical facilities. Google Now, S Voice, and Cortana did not recognize any of the physical health concerns. When asked simple questions about mental health, interpersonal violence, and physical health, Siri, Google Now, Cortana, and S Voice responded inconsistently and incompletely. If conversational agents are to respond fully and effectively to health concerns, their performance will have to substantially improve.

Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application.

PubMed

Mandel, Jacob E; Morel-Ovalle, Louis; Boas, Franz E; Ziv, Etay; Yarmohammadi, Hooman; Deipolyi, Amy; Mohabir, Heeralall R; Erinjeri, Joseph P

2018-02-20

The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient's home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution's catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29 min during non-rush hour (p = 0.03), and 28.80 min during rush hour (p < 0.001). Using a custom Google Maps application to schedule outpatients for IR procedures can effectively reduce patient travel time when more than one location providing IR procedures is available within the same hospital system.

Assessing species distribution using Google Street View: a pilot study with the Pine Processionary Moth.

PubMed

Rousselet, Jérôme; Imbert, Charles-Edouard; Dekri, Anissa; Garcia, Jacques; Goussard, Francis; Vincent, Bruno; Denux, Olivier; Robinet, Christelle; Dorkeld, Franck; Roques, Alain; Rossi, Jean-Pierre

2013-01-01

Mapping species spatial distribution using spatial inference and prediction requires a lot of data. Occurrence data are generally not easily available from the literature and are very time-consuming to collect in the field. For that reason, we designed a survey to explore to which extent large-scale databases such as Google maps and Google Street View could be used to derive valid occurrence data. We worked with the Pine Processionary Moth (PPM) Thaumetopoea pityocampa because the larvae of that moth build silk nests that are easily visible. The presence of the species at one location can therefore be inferred from visual records derived from the panoramic views available from Google Street View. We designed a standardized procedure allowing evaluating the presence of the PPM on a sampling grid covering the landscape under study. The outputs were compared to field data. We investigated two landscapes using grids of different extent and mesh size. Data derived from Google Street View were highly similar to field data in the large-scale analysis based on a square grid with a mesh of 16 km (96% of matching records). Using a 2 km mesh size led to a strong divergence between field and Google-derived data (46% of matching records). We conclude that Google database might provide useful occurrence data for mapping the distribution of species which presence can be visually evaluated such as the PPM. However, the accuracy of the output strongly depends on the spatial scales considered and on the sampling grid used. Other factors such as the coverage of Google Street View network with regards to sampling grid size and the spatial distribution of host trees with regards to road network may also be determinant.

Assessing Species Distribution Using Google Street View: A Pilot Study with the Pine Processionary Moth

PubMed Central

Dekri, Anissa; Garcia, Jacques; Goussard, Francis; Vincent, Bruno; Denux, Olivier; Robinet, Christelle; Dorkeld, Franck; Roques, Alain; Rossi, Jean-Pierre

2013-01-01

Mapping species spatial distribution using spatial inference and prediction requires a lot of data. Occurrence data are generally not easily available from the literature and are very time-consuming to collect in the field. For that reason, we designed a survey to explore to which extent large-scale databases such as Google maps and Google street view could be used to derive valid occurrence data. We worked with the Pine Processionary Moth (PPM) Thaumetopoea pityocampa because the larvae of that moth build silk nests that are easily visible. The presence of the species at one location can therefore be inferred from visual records derived from the panoramic views available from Google street view. We designed a standardized procedure allowing evaluating the presence of the PPM on a sampling grid covering the landscape under study. The outputs were compared to field data. We investigated two landscapes using grids of different extent and mesh size. Data derived from Google street view were highly similar to field data in the large-scale analysis based on a square grid with a mesh of 16 km (96% of matching records). Using a 2 km mesh size led to a strong divergence between field and Google-derived data (46% of matching records). We conclude that Google database might provide useful occurrence data for mapping the distribution of species which presence can be visually evaluated such as the PPM. However, the accuracy of the output strongly depends on the spatial scales considered and on the sampling grid used. Other factors such as the coverage of Google street view network with regards to sampling grid size and the spatial distribution of host trees with regards to road network may also be determinant. PMID:24130675

Custom Search Engines: Tools & Tips

ERIC Educational Resources Information Center

Notess, Greg R.

2008-01-01

Few have the resources to build a Google or Yahoo! from scratch. Yet anyone can build a search engine based on a subset of the large search engines' databases. Use Google Custom Search Engine or Yahoo! Search Builder or any of the other similar programs to create a vertical search engine targeting sites of interest to users. The basic steps to…

Assessing Homegrown Library Collections: Using Google Analytics to Track Use of Screencasts and Flash-Based Learning Objects

ERIC Educational Resources Information Center

Betty, Paul

2009-01-01

Increasing use of screencast and Flash authoring software within libraries is resulting in "homegrown" library collections of digital learning objects and multimedia presentations. The author explores the use of Google Analytics to track usage statistics for interactive Shockwave Flash (.swf) files, the common file output for screencast and Flash…

Innovative Instructional Tools from the AMS

NASA Astrophysics Data System (ADS)

Abshire, W. E.; Geer, I. W.; Mills, E. W.; Nugnes, K. A.; Stimach, A. E.

2016-12-01

Since 1996, the American Meteorological Society (AMS) has been developing online educational materials with dynamic features that engage students and encourage additional exploration of various concepts. Most recently, AMS transitioned its etextbooks to webBooks. Now accessible anywhere with internet access, webBooks can be read with any web browser. Prior versions of AMS etextbooks were difficult to use in a lab setting, however webBooks are much easier to use and no longer a hurdle to learning. Additionally, AMS eInvestigations Manuals, also in webBook format, include labs with innovative features and educational tools. One such example is the AMS Climate at a Glance (CAG) app that draws data from NOAA's Climate at a Glance website. The user selects historical data of a given parameter and the app calculates various statistics revealing whether or not the results are consistent with climate change. These results allow users to distinguish between climate variability and climate change. This can be done for hundreds of locations across the U.S. and on multiple time scales. Another innovative educational tool used in AMS eInvestigations Manuals is the AMS Conceptual Climate Energy Model (CCEM). The CCEM is a computer simulation designed to enable users to track the paths that units of energy might follow as they enter, move through and exit an imaginary system according to simple rules applied to different scenarios. The purpose is to provide insight into the impacts of physical processes that operate in the real world. Finally, AMS educational materials take advantage of Google Earth imagery to reproduce the physical aspects of globes, allowing users to investigate spatial relationships in three dimensions. Google Earth imagery is used to explore tides, ocean bottom bathymetry and El Nino and La Nina. AMS will continue to develop innovative educational materials and tools as technology advances, to attract more students to the Earth sciences.

Use of Web 2.0 Technologies for Public Outreach on a Simulated Mars Mission

NASA Astrophysics Data System (ADS)

Shiro, B.; Palaia, J.; Ferrone, K.

2009-12-01

Recent advances in social media and internet communications have revolutionized the ways people interact and disseminate information. Astronauts are already starting to take advantage of these tools by blogging and tweeting from space, and almost all NASA missions now have presences on the major social networking sites. One priority for future human explorers on Mars will be communicating their experiences to the people back on Earth. During July 2009, a six-member crew of volunteers carried out a simulated Mars mission at the Flashline Mars Arctic Research Station (FMARS) on Devon Island in the Canadian Arctic. Living in a habitat, conducting EVAs wearing spacesuits, and observing communication delays with “Earth,” the crew endured restrictions similar to those that will be faced by future human Mars explorers. Throughout the expedition, crewmembers posted regular blog entries, reports, photos, videos, and updates to their website and social media outlets Twitter, Facebook, YouTube, and Picasa Web Albums. During the sixteen EVAs of their field science research campaign, FMARS crewmembers collected GPS track information and took geotagged photos using GPS-enabled cameras. They combined their traverse GPS tracks with photo location information into KML/KMZ files that website visitors can view in Google Maps or Google Earth. Although the crew observed a strict 20-minute communication delay with “Earth” to simulate a real Mars mission, they broke this rule to conduct four very successful live webcasts with student groups using Skype since education and public outreach were important objectives of the endeavor. This presentation will highlight the use of Web 2.0 technologies for public outreach during the simulated Mars expedition and the implications for other remote scientific journeys. The author embarks on a "rover" to carry out an EVA near the FMARS Habitat. The satellite dish to the right of the structure was used for all communications with the remote outpost.

Mars for Earthlings: an analog approach to Mars in undergraduate education.

PubMed

Chan, Marjorie; Kahmann-Robinson, Julia

2014-01-01

Mars for Earthlings (MFE) is a terrestrial Earth analog pedagogical approach to teaching undergraduate geology, planetary science, and astrobiology. MFE utilizes Earth analogs to teach Mars planetary concepts, with a foundational backbone in Earth science principles. The field of planetary science is rapidly changing with new technologies and higher-resolution data sets. Thus, it is increasingly important to understand geological concepts and processes for interpreting Mars data. MFE curriculum is topically driven to facilitate easy integration of content into new or existing courses. The Earth-Mars systems approach explores planetary origins, Mars missions, rocks and minerals, active driving forces/tectonics, surface sculpting processes, astrobiology, future explorations, and hot topics in an inquiry-driven environment. Curriculum leverages heavily upon multimedia resources, software programs such as Google Mars and JMARS, as well as NASA mission data such as THEMIS, HiRISE, CRISM, and rover images. Two years of MFE class evaluation data suggest that science literacy and general interest in Mars geology and astrobiology topics increased after participation in the MFE curriculum. Students also used newly developed skills to create a Mars mission team presentation. The MFE curriculum, learning modules, and resources are available online at http://serc.carleton.edu/marsforearthlings/index.html.