IN43A-0899
An Archive of Science KML Datasets
Google Earth has become a worldwide phenomena that has opened millions of people's eyes to the potential capabilities of Virtual Globes. However, it is the concurrent development of Keyhole Markup Language (KML) that has provided the best method for people to visualize their own datasets within these Globes. This has been particularly true for the earth sciences where there is an abundance of geographically located data, but not every researcher or educator has the resources or technical expertise to manipulate this data using traditional GIS resources. KML provides a simple method for users to visualize their data in Google Earth, Google Maps, and since its Open Geospatial Consortium designation as a best practice, an increasing number of other Geobrowsers. In the case of the Google browsers many KML features can be created directly through the interface and don't even require the user to directly view any code. As both KML capabilities and scientists' understanding of the language have evolved, visualizations that have become more dynamic and increasingly complex. These creations have come to the attention of other scientists and the public general through the Google Earth Community forum. Many interesting and imaginative uses of KML in science exist on these pages, but locating them can often be difficult due to the large volume of content. Google's KML archive and Google Earth Blog's science listings have provided filtering for much of this content, but opportunity still exists for a quality controlled archive of science-dedicated KML. At the University of Alaska we are developing an archive that fills this need. http://kml.images.alaska.edu
IN43A-0900
Integrating the "Street View" Imagery Viewer With Google Maps
Integration of the flash-based "Street View" viewer within Google Maps provides, in effect, a geobrowser within a geobrowser and solves a tough UI problem for rendering different types of data within the same screen. This presentation demonstrates this new technology and suggests some ideas for future work.
IN43A-0901
Google Mapplets for Earthquakes and Volcanic Activity
The USGS Earthquake and Volcano Hazards Programs monitor, assess, and issue warnings of natural hazards. Users can access our hazards information through our web pages, RSS feeds, and now through USGS Mapplets. Mapplets allow third party data layers to be added on top of Google Maps™ (http://maps.google.com - My Maps tab). Mapplets are created by parsing a GeoRSS feed, which involves searching through an XML file for location data and plotting the associated information on a map. The new Mapplets allow users to view both real-time earthquakes and current volcanic activity on the same map for the first time. In addition, the USGS Mapplets have been added to Google's extensive collection of Mapplets, allowing users to add the types of information they want to see on their own customized maps. The Earthquake Mapplet plots the past week of earthquakes around the world, showing the location, time and magnitude. The Volcano Mapplet displays the latest U.S. volcano updates, including the current level of both ground-based and aviation hazards. Join us to discuss how Mapplets are made and how they can be used to create your own customized map. http://google-latlong.blogspot.com/2007/08/posted-by-scott-haefner-and-dina.html
IN43A-0902
Alaska Energy Inventory Project: Consolidating Alaska's Energy Resources
Alaska has considerable energy resources distributed throughout the state including conventional oil, gas, and coal, and unconventional coalbed and shalebed methane, gas hydrates, geothermal, wind, hydro, and biomass. While much of the known large oil and gas resources are concentrated on the North Slope and in the Cook Inlet regions, the other potential sources of energy are dispersed across a varied landscape from frozen tundra to coastal settings. Despite the presence of these potential energy sources, rural Alaska is mostly dependent upon diesel fuel for both electrical power generation and space heating needs. At considerable cost, large quantities of diesel fuel are transported to more than 150 roadless communities by barge or airplane and stored in large bulk fuel tank farms for winter months when electricity and heat are at peak demands. Recent increases in the price of oil have severely impacted the price of energy throughout Alaska, and especially hard hit are rural communities and remote mines that are off the road system and isolated from integrated electrical power grids. Even though the state has significant conventional gas resources in restricted areas, few communities are located near enough to these resources to directly use natural gas to meet their energy needs. To address this problem, the Alaska Energy Inventory project will (1) inventory and compile all available Alaska energy resource data suitable for electrical power generation and space heating needs including natural gas, coal, coalbed and shalebed methane, gas hydrates, geothermal, wind, hydro, and biomass and (2) identify locations or regions where the most economic energy resource or combination of energy resources can be developed to meet local needs. This data will be accessible through a user-friendly web-based interactive map, based on the Alaska Department of Natural Resources, Land Records Information Section's (LRIS) Alaska Mapper, Google Earth, and Terrago Technologies' GeoPDF format to display the location, type, and where applicable, a risk-weighted quantity estimate of energy resources available in a given area or site. The project will be managed and directed by the DNR Division of Geological and Geophysical Surveys DGGS over the next five years with a team composed of the Alaska Energy Authority, DNR Division of Forestry, and DNR LRIS. http://energyinventory.alaska.gov
IN43A-0903
From data to information: Tools and techniques educators can use to enhance Google Earth imagery with geographic information systems data and three dimensional models
As with any educational technology, moving beyond basic information delivery to dynamic use can be a challenge and Google Earth (GE) is no exception. Moving beyond annotated placemarks and pictures, educators can utilize free, free-to-educators, and low cost tools to develop learning experiences within GE to facilitate dynamic interaction with real world data in the form of three dimensional models (3D) and geographic information systems data (GIS). Students take an active role in knowledge construction through self-directed navigation in 3D, seeing features of the landscape not in snapshot views found in textbooks, but in situ and in context. By incorporating categorized data, such as what is commonly found in GIS, an added dimension of human interaction can be incorporated. For example, GIS layers such as landuse, soil type, etc. provide students with data tools for investigating the role their community plays in supporting migrating Monarch butterfly habitat. Functionality for changing the appearance of layers in GE facilitates interaction with geospatial data in a manner that creates a type of "visual" GIS and can serve as an advanced organizer for later use of more powerful GIS software. GE can also be used as a metaphor to create a new context for an otherwise abstract concept, for example, scaling 3D models of the sun and planets to the size of a well known football stadium and placing each planet at the corresponding scaled distances from that location. Photorealistic 3D models created using SketchUp and Anim8or may help students relate to an otherwise abstract concept of planetary size and distances. Finally, digital elevation models (DEM) draped with imagery not available in GE or with GIS data can be used to make topic-specific 3D models either used within GE or in a 3D model viewer embedded in a website or email. With a little instruction, students can quickly learn how to make their own models as well. Procedures and software to accomplish each of these examples will be demonstrated.
IN43A-0904 INVITED
Five Geobrowsing Lesson Plans
Virtual globes such as Google Earth or NASA World Wind may be used as is, without KML coding or inclusion of three-dimensional models, to design effective learning experiences. With KML coding and Collada modeling, sophisticated learning objects may be developed. Five examples are presented for interactive demonstration, covering a range of student levels of ability: (i) "Wait, Don't Tell Me!" Students predict locations on the globe given Lat / Lon or UTM data and then confirm their judgments using "Fly to" (ii) "Where on Earth?" Students search for features on the virtual globe given images, data, and/or models. (iii) "Tsunami!" Students react to modeled real-time data feeds and decide whether to issue an natural hazard alert. (iv) "To the Rescue!" Students estimate food, water, and housing needs resulting from a natural disater and plan rescue and relief operations. (v) "Just Map It!" Students overlay their own field data on the virtual terrain and create solid models of geological structures.
IN43A-0905
Real-time shipboard displays for science operation and planning on CGC Healy
To facilitate effective science planning and decision making, we have developed a real-time geospatial browser and other displays widely used by many if not all members of USCGC Healy's science cruises and some officers and crew since 2004. In order to enable a 'zero-configuration' experience to the end user with nearly any modern browser, on any platform, anywhere on the ship with wired (or wireless) network access, we chose a Web-based/server-centric approach that provides a very low barrier to access in an environment where we have many participants constantly coming and going, often with their own computers. The principle interface for planning and operational decision making is a georeferenced, Web-based user interface built on the MapServer Web GIS platform developed at the University of Minnesota (http://mapserver.gis.umn.edu/), using the PostGIS spatial database extensions (http://postgis.refractions.net/) to enable live database connectivity. Data available include current ship position and orientation, historical ship tracks and data, seafloor bathymetry, station locations, RADARSAT, and subbottom profiles among others. In addition to the user interfaces that are part of individual instrumentation (such as the sonars and navigation systems), custom interfaces have been developed to centralize data with high update rates such as sea surface temperature, vessel attitude, position, etc. Underlying data acquisition and storage is provided by the Lamont Data System (LDS) and the NOAA SCS system. All data are stored on RAIDed disk systems and shared across a switched network with a gigabit fiber backbone. The real-time displays access data in a number of ways including real-time UDP datagrams from LDS, accessing files on disk, and querying a PostgreSQL relational backend. This work is supported by grants from the U.S. National Science Foundation, Office of Polar Programs, Arctic Science section. http://ilab.ldeo.columbia.edu/projects/healy/
IN43A-0906
Google Earth Visualizations of the Marine Automatic Identification System (AIS): Monitoring Ship Traffic in National Marine Sanctuaries
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.
IN43A-0907
Using Google Earth to Explore Strain Rate Models of Southern California
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. http://www.eserc.stonybrook.edu/Strain/GoogleEarth/
IN43A-0908
Automated Generation of 3D Volcanic Gas Plume Models for Geobrowsers
A network of five UV spectrometers on Etna automatically gathers column amounts of SO2 during daylight hours. Near-simultaneous scans from adjacent spectrometers, comprising 210 column amounts in total, are then converted to 2D slices showing the spatial distribution of the gas by tomographic reconstruction. The trajectory of the plume is computed using an automatically-submitted query to NOAA's HYSPLIT Trajectory Model. This also provides local estimates of air temperature, which are used to determine the atmospheric stability and therefore the degree to which the plume is dispersed by turbulence. This information is sufficient to construct an animated sequence of models which show how the plume is advected and diffused over time. These models are automatically generated in the Collada Digital Asset Exchange format and combined into a single file which displays the evolution of the plume in Google Earth. These models are useful for visualising and predicting the shape and distribution of the plume for civil defence, to assist field campaigns and as a means of communicating some of the work of volcano observatories to the public. The Simultaneous Algebraic Reconstruction Technique is used to create the 2D slices. This is a well-known method, based on iteratively updating a forward model (from 2D distribution to column amounts). Because it is based on a forward model, it also provides a simple way to quantify errors.
IN43A-0909
Operational volcanic ash tracking and dispersion model predictions within Virtual Globes
Volcanic ash tracking and dispersion (VATD) models are routinely used by volcano observatories and volcanic ash advisory centers (VAAC) to analyze and predict the movement of airborne ash world-wide. Within the North Pacific Region (NOPAC), there are three models used (CanERM, HYSPLIT and Puff), each with their own display environments. Virtual Globes, here we use Google Earth™, provide an easy to use universal tool for displaying the VATD simulations. The addition of the ‘timestamp' option allows the VATD simulations to be displayed in real- time as static images or as animations that show the predicted position of the ash clouds. Here, we display Puff model operational predictions in both two and three dimensions for volcanoes across the NOPAC and selected volcanoes in Europe, Indonesia, Central and South America. The Puff model predictions are produced automatically every 3 - 6 hrs and displayed directly within Google Earth and Maps™. In addition, we show how any Puff model prediction this is accomplished using an online interface. These displays of the VATD model predictions in Virtual Globes can be used to effectively and informatively illustrate the ash clouds location. Then the ash cloud location can be assessed in its proximity to local population, airports and any possible air traffic.
IN43A-0910
NASA A-Train Vertical Data (Curtains) in Google Earth
Google Earth combines satellite imagery, aerial photography, map data, and human-social data to make a real 3D interactive template of the world. It is revolutionizing the way that general public recognize our planet and professional scientists discover, add, and share information about different geographic-related subjects in the world. NASA Goddard Earth Science (GES) Data and Information Service Center (DISC) has done innovative work integrating NASA imagery in Google Earth in order to facilitate scientific research and releasing of geospatial- related public information. The NASA imagery includes two dimensional (2D) flat data and three dimensional (3D) vertical data. Here, a new solution is introduced to integrate the vertical data from the A-Train constellation satellites CloudSat, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation), and Aqua (mainly MODIS and AIRS products) into Google Earth to vividly expose cloud, aerosol, and H2O characteristics and atmospheric temperature profile in the form of curtain along the satellite orbit. All kinds of vertical data are first processed by GIOVANNI (GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure) A-Train system based on user-selected spatial/temporal range and physical parameters. The resultant image is processed into transparent small image slices with each image slice representing the fixed temporal internal orbit range. A generalized COLLADA (COLLAborative Design Activity) 3D model is designed to render the image slices in the form of 3D. Based on the designed COLLADA models and satellite orbit coordinates, an orbit model is designed and implemented in KML (Keyhole Markup Language) format. The resultant orbit curtain makes vertical data viewable, transparently or opaquely, in Google Earth. Thus, three- dimensional science research data can be made available to scientists and the general public in a popular venue. Also, simultaneous visualization and efficient exploration of the relationships among quantitative geospatial data (e.g. comparing the vertical data profiles with MODIS, AIRS data and TRMM precipitation data) becomes possible. This method allows combining vertical data together with other geospatial data for scientific research and allows better understanding of our planet. A key capability of the system is the ability to visualize and compare diverse, simultaneous data from different providers, revealing new information and knowledge that would otherwise be hidden. http://disc.sci.gsfc.nasa.gov/atdd/
IN43A-0911
Innovative Uses of Google Earth to Facilitate Scientific Understanding of Meteorological Observations, Forecasts and Analyses
The Google Earth™ application provides a unique means to display, animate and layer imagery and geophysical data on a 3-dimensional globe without the distortions imparted by a flat display. Using Google Earth™, high resolution imagery from environmental satellite data such as the MODIS sensor onboard EOS Terra and Aqua can be viewed at various levels of detail, and updated dynamically as new datasets arrive. Observations and numerical weather prediction model forecasts can be viewed and directly compared. It also provides a forum for training and education of geophysical concepts (atmospheric and space weather, land surface processes, climate, oceanography, etc.) by fusing the aspects of a web-browser with the capability to geo-reference and geo- fuse multiple layers of data. This poster shows several examples that demonstrate how the Google Earth™ application can be used to display meteorological datasets. For example, an animation of satellite rainfall images from a blend of satellite types gives the user an immediate indication of heavy rain and flooding. 3-D aircraft flight tracks from a recent field experiment show how the in-situ data gathered onboard the aircraft can be compared with coordinated ground-based observations. We also demonstrate how to display data selected from a webpage directly into Google Earth™, a land cover database integrated with active fire data, and the use of multispectral and multi- resolution satellite data as you zoom in on a tropical cyclone.