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International Journal of Remote Sensing

ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: https://www.tandfonline.com/loi/tres20

Google Earth as a virtual globe tool for Earthscience applications at the global scale: progressand perspectives

Le Yu & Peng Gong

To cite this article: Le Yu & Peng Gong (2012) Google Earth as a virtual globe tool for Earthscience applications at the global scale: progress and perspectives, International Journal of RemoteSensing, 33:12, 3966-3986, DOI: 10.1080/01431161.2011.636081

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Published online: 09 Dec 2011.

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International Journal of Remote SensingVol. 33, No. 12, June 2012, 3966–3986

Review Article

Google Earth as a virtual globe tool for Earth science applicationsat the global scale: progress and perspectives

LE YU† and PENG GONG*†‡†Ministry of Education, Key Laboratory for Earth System Modelling, Centre for Earth SystemScience, Institute for Global Change Studies, Tsinghua University, Beijing 100084, PR China‡Department of Environmental Science, Policy and Management, University of California,

Berkeley, CA 94720-3114, USA

(Received 5 April 2011; in final form 11 October 2011)

Research on global environmental change requires new data processing and anal-ysis tools that can integrate heterogeneous geospatial data from real-time in situmeasurement, remote sensing (RS) and geographic information systems (GISs) atthe global scale. The rapid growth of virtual globes for global geospatial informa-tion management and display holds promise to meet such a requirement. Virtualglobes, Google Earth in particular, enable scientists around the world to communi-cate their data and research findings in an intuitive three-dimensional (3D) globalperspective. Different from traditional GIS, virtual globes are low cost and easy touse in data collection, exploration and visualization. Since 2005, a considerablenumber of papers have been published in peer-reviewed journals and proceed-ings from a variety of disciplines. In this review, we examine the development andapplications of Google Earth and highlight its merits and limitations for Earth sci-ence studies at the global scale. Most limitations are not unique to Google Earth,but to all virtual globe products. Several recent efforts to increase the function-alities in virtual globes for studies at the global scale are introduced. The powerof virtual globes in their current generations is mostly restricted to functions as a‘geobrowser’; a better virtual globe tool for Earth science and global environmentalchange studies is described.

1. Introduction

Global environmental change has become an issue attracting wide concern in thescientific and policy-making community. Earth system science is developed as an inter-disciplinary science to address global environmental change issues (Leemans et al.2009, Reid et al. 2010). Significant technological advancement is needed to providethe measurement, monitoring, modelling, analysis and assessment tools for solvingproblems at global scales. The integration of real-time local in situ measurement andmonitoring with large-scale Earth observations and the modelling of the Earth systemat the global scale have become the new frontiers in Earth science (Szewczyk et al.2004, Tooth 2006, Barker et al. 2007, Gong 2007, Guralnick et al. 2007, Aanensen etal. 2009, Liang et al. 2010, De Paor and Whitmeyer 2011).

To collect, manage and display global environmental data, traditional geographicinformation science and technologies are faced with new challenges. These include (1)

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis

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Google Earth for Earth science 3967

easy-to-use, practical and operational three-dimensional (3D) global representationand visualization of geospatial data from local to global scales; (2) efficient algorithmsthat can handle huge volumes of multisource data for global scale information extrac-tion and change monitoring; and (3) spatial–temporal techniques that can integrateobservation data into process-based models.

To address the first challenge, virtual globe technologies such as Google Earth,Microsoft Virtual Earth and the National Aeronautics and Space Administration(NASA) World Wind have made the first attempts and achieved substantial progressduring the past decade. A considerable number of technologies developed in Earthscience fields such as atmospheric and oceanographic sciences and even epidemiol-ogy can be borrowed to move forward geographic information technologies to meetthe third challenge (Xu et al. 2006, Hu et al. 2010, Shapiro et al. 2010). To addressthe second challenge, it is critically important to integrate real-time in situ field data,remotely sensed data and geographic information system (GIS)-based data process-ing and analysis tools into a systematic framework aiming at handling heterogeneousgeospatial data at the global scale. Such a framework would bring significant benefitto studies in Earth system science, especially for global change research and globalepidemiological studies (Gong et al. 2011). In this review, we survey efforts made toaddress the first challenge with special attention paid to the contributions of GoogleEarth to scientific applications.

Visualization of heterogeneous data sources has the benefit of revealing new insightsinto the patterns of nature/human-related phenomena and understanding the Earth’sdynamics. For this purpose, GISs are useful tools for collating, exploring, visualiz-ing and analysing geospatial data (e.g. Longley et al. 2001, Al-Sabhan et al. 2003,Malczewski 2004, Rinaldi et al. 2006). The integration of remotely sensed envi-ronmental data into a GIS platform can assist in a better understanding of thespatial–temporal dynamics of a wide range of Earth/ecological/disease systems, espe-cially those with spatial/environmental correlates (Boyd and Foody 2011). However,traditional GIS software is expensive and has a steep learning curve (Conroy et al.2008, Oberlies et al. 2009, Renner et al. 2009). In addition, it is less flexible for geovi-sualization (Wood et al. 2007) and it is difficult to handle and integrate huge volumesof data from different sources automatically and seamlessly. The development of vir-tual globe technology has provided access to a low-cost (even free) and easy-to-usemethod to communicate geospatial data more effectively to the general public, as wellas among scientists (Stensgaard et al. 2009).

To the scientific community, virtual globes such as Google Earth, NASA’s WorldWind and ArcGIS Explorer are not only tools providing huge volumes of freely avail-able images and 3D views of the Earth, but more importantly are effective channelsto communicate research findings. Virtual globes offer researchers a simpler alterna-tive to traditional GIS software, leading to increased data sharing while facilitatingstudies at the global scale. Thousands of papers and reports have been published toelucidate the use of virtual globes in diverse fields since the emergence of several majorvirtual globes around 2005. This new technology has been introduced and reviewed ina number of peer-reviewed papers (e.g. Boulos 2005, Craglia et al. 2008, Foresman2008, Goodchild 2008, Stensgaard et al. 2009) and sessions in academic meetings (e.g.American Geophysical Union (AGU) Fall meeting).

In this review, we examine the development of virtual globes, specifically GoogleEarth, which is the most popular virtual globe in both public and scientific communi-ties, review its applications to Earth science and discuss the merits and limitations of

3968 L. Yu and P. Gong

this product (at its current stage) for global studies. Several possible improvements areidentified at the end.

2. Virtual globe: a brief history

A virtual globe is a 3D representation of the Earth with the ability to (1) explorein a virtual environment, (2) add users’ own data and share them with others and(3) represent natural and man-made features on the surface of the Earth (Wikipedia2010). Virtual globes provide easy access to image and terrain data and user-friendlyannotation abilities.

2.1 Developments

In the 1960s, Buckminster Fuller, an American architect, first proposed the ideathat computers could be used to model a virtual Earth and the concept of aGeoscope (http://en.wikipedia.org/wiki/Geoscope) as a large spherical display (Baileyand Chen 2011). In 1992, the above vision was conceptualized as ‘Digital Earth’by Gore (1992), who described a digital future where people could interact witha computer-generated 3D virtual globe and access vast amounts of scientific andcultural information to help them understand the Earth and its human activities(http://en.wikipedia.org/wiki/Digital_Earth). The boom of virtual globes resultedfrom a confluence of technological advances (e.g. the demands of the gaming com-munity had led to the development of high-performance graphics cards that hadbecome part of desktop computer systems; broadband internet had been developedfor transmission of large volume data) and the availability of high-resolution data(the launches of the IKONOS (1999) and QuickBird (2001) satellites ushered in anew era of Earth image acquisition and through these platforms data with a spatialresolution of a few metres or less became commercially available), which are directlyfacilitated by US spatial data policies since the formation of the Land Remote SensingPolicy Act of 1992 (http://geo.arc.nasa.gov/sge/landsat/15USCch82.html). This pol-icy permitted private companies to enter the satellite imaging business. Since then, anumber of privately owned satellites have been launched including IKONOS, EarlyBird 1, QuickBird, WorldView-1, WorldView-2, OrbView-2, OrbView-3, GeoEye-1and GeoEye-2. As more and more high-resolution satellite data and aerial imagesbecome accessible, many of the latest virtual globes are built to retrieve and dis-play these images. Nevertheless, roaming high-resolution images on a spherical Earthmodel is not the only function of virtual globes.

2.2 Functions of virtual globes

There are many virtual globes available today. As tabulated in Wikipedia(http://en.wikipedia.org/wiki/Virtual_globe), major virtual globe software (i.e.Google Earth, NASA World Wind, Skyline Globe), al though different in afew features (such as multiple data sets, street maps, guides for local land-marks, real-time traffic reports, 3D building models and sky modes), sharevery similar functionalities (such as providing satellite/aerial images, topo-graphic maps, Global Positioning System (GPS) integration, distance measure-ment, movie makers, 3D graphics and topography and Wikipedia integra-tion). A brief introduction to online geoinformatics services and other virtualglobes is available in Boulos (2005), and updated information in Wikipedia


(http://en.wikipedia.org/wiki/Virtual_globe). Detailed comparisons of many majorvirtual globes can be found from http://en.wikipedia.org/wiki/Virtual_globe andhttp://worldwindcentral.com/wiki/Product_Comparison. For capturing a general viewof contemporary virtual globe infrastructures, several featured virtual globes arebriefly introduced in §2.4.

2.3 Scientific values of virtual globes: maps and beyond

Virtual globes have opened the world of satellite images to the general public andallowed entertainment, education and exploration of new findings (e.g. Pringle 2010).The results of a survey among both kindergarten to grade 12 students and non-expertusers show that most individuals use these technologies simply for observational pur-poses (Schoning et al. 2008). Besides, the value of this technology to the academiccommunity has been identified at very early stage following the release of GoogleEarth (Binder 2005, Boulos 2005, Heyman 2005). To researchers, virtual globes arenot only tools that provide huge volumes of freely available images, and 3D views,of the Earth, but more importantly a place to communicate their research findingsand information that the public is interested in, for example weather threat analysisinformation (Smith and Lakshmanan 2011).

The most straightforward way scientists use a virtual globe is by laying theirown data (e.g. field collections, multisource input data sets, outputs after process-ing, modelling results) on top of background imagery and zooming from spaceright down to specific locations to explore the geographical context (Oberlies et al.2009). Scientists are already experimenting with these tools to showcase their col-lected data sets and research findings to themselves, peers and the public in visuallyappealing ways. Furthermore, virtual globes provide highly effective methods of pre-senting and communicating the understanding of fundamental Earth science conceptsand the dynamic processes of Earth systems, for example global climate change(http://www.google.com/landing/cop15/). Besides, having GIS integrated with virtualglobes, scientists can exploit the full capacities of GIS to analyse vast arrays ofdisparate data in their spatial context (e.g. Chang et al. 2009, Neutens et al. 2010).

2.4 Major virtual globes

Google Earth (http://www.google.com/earth/index.html) (released in 2005) is the mostinfluential virtual globe. It was originally called EarthViewer 3D and was created byKeyhole, Inc., a company acquired by Google in 2004. User data can be incorporatedinto the system in a variety of ways, most notably using Keyhole Markup Language(KML). There are three versions of Google Earth, namely Google Earth Free, GoogleEarth Pro and Google Earth Enterprise, listed in rising order of supported capabili-ties. Google Earth Pro (http://www.google.com/enterprise/earthmaps/earth_pro.html)is a business-oriented subscription-only upgrade to Google Earth Free thatprovides customers with additional features in GIS and remote-sensing (RS)data import, advanced measurement tools, higher data download speedsand higher resolution printing and movie making. Google Earth Enterprise(http://www.google.com/enterprise/earthmaps/earth_enterprise.html) is designed foruse by organizations that have satellite imagery or large quantities of geospatialdata that need to be deployed in a secure solution (e.g. run on their own hostingenvironment on their own servers).


Unlike Google Earth, which is commercial software, NASA World Wind(http://worldwind.arc.nasa.gov) (released in 2004) is an open source virtual globe.NASA World Wind Java Software Development Kit (SDK) enables users tocreate standalone applications in which users’ own data and models can bepresented in the context of a multiresolution model of a globe. Many globesbased on NASA World Wind have been developed, for example Virtual Ocean(http://www.virtualocean.org/), Geosoft Dapple (http://dapple.geosoft.com/default.asp), PYXIS software (http://www.pyxisinnovation.com/), WW2D (http://www.alpix.com/3d/worldwin/WW2d_Java.html), Punt (http://punt.sourceforge.net/new_svn/index.html) and SERVIR-VIZ (http://www.iagt.org/downloads.aspx/).

As a response to Google Earth, Environmental Systems Research Institute, Inc.(ESRI) produced its own virtual globe – ArcGIS Explorer (http://www.esri.com/software/arcgis/explorer/index.html). It comes equipped with a series of analytic toolsvia ArcGIS Server, which supplies mapping and GIS capabilities via ArcGIS Onlinefor ESRI’s web and client applications. User data and models can be imported fromany of the other ArcGIS products into ArcGIS Explorer directly. However, whilemost virtual globes are cross-platform applications, ArcGIS Explorer runs only onMicrosoft Windows platforms, requiring. NET and Internet Explorer.

Other notable virtual globes include Microsoft’s Bing Maps Platform (previouslyMicrosoft Virtual Earth) (http://www.bing.com/maps), Skyline Globe (http://www.skylineglobe.com/), CitySurf Globe (http://www.citysurf.com.tr/en/index.asp), LeicaVirtual Explorer (http://gi.leica-geosystems.com/LGISub1x251x0.aspx), Marble(http://edu.kde.org/marble/) etc. It is worth mentioning that many countries developednational virtual globes as a portal for national spatial data, for example, French-Geoportail (http://www.geoportail.fr/), India-Bhuvan (http://bhuvan.nrsc.gov.in/)and China-Map World (http://tianditu.cn/ and http://chinaonmap.cn/).

3. Google Earth

As noted above, Google Earth is the most popular virtual globe software (also see§3.1). A detailed examination of its contribution to the scientific community and anal-ysis of its merits and limitations could reflect the whole picture of virtual globes.Besides the innovative techniques of sphere visualization (Tanner et al. 1998, Bar-Zeev 2007), another technique that is a favourite of the users of Google Earth isKML (Bailey and Chen 2011); it allows user-defined data sets to be overlaid in GoogleEarth. Google Earth was the first program able to view and graphically edit KML files.Nowadays, more and more virtual globes and GIS software support KML, especiallyafter KML became an Open Geospatial Consortium (OGC) standard in 2008. KMLhas been the most widely embraced means by which scientific users create dynamic,interactive displays without the need to be GIS experts or computer programmers(Bailey and Chen 2011). Furthermore, a COLLADA (Collaborative Design Activity)-based 3D KML model has opened doors for vertical profile rendering in GoogleEarth. It improves the visualization effects for complex objects and phenomena.

3.1 Google Earth in the literature

To obtain an overview of the growing use of virtual globes in the literature, we searcheda widely used electronic database, namely the Institute for Scientific Information(ISI) Web of Science (http://apps.isiknowledge.com) on January 2011 for the period


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of 1 January 2005 to 31 December 2010. Since the launch of Google Earth in mid-2005, a steady increase of publications has been noted (figure 1). NASA World Wind,ArcGIS Explorer and Microsoft Virtual Earth (Bing Maps) were also searched in thesame database. It is quite obvious that Google Earth is the favourite virtual globeamong academic users, comparing the paper numbers of these four globes, as shownin figure 1.

3.2 Google Earth-based applications

Since its introduction in 2005, Google Earth has found numerous applica-tions in a wide variety of scientific disciplines. Google Earth-related scientificresearch can be found in many conference sessions (Virtual Globes in Science,http://conferences.images.alaska.edu/), including the AAG (Association of AmericanGeographers), AGU, AMS (American Meteorological Society) and GSA (GeologicalSociety of America). Particularly, virtual globes were hot topics at the AnnualFall Meetings of the AGU from 2006 to 2009. Nearly 200 presentations (avail-able at (1) year 2006: http://conferences.images.alaska.edu/agu/2006/, (2) year 2007:http://conferences.images.alaska.edu/agu/2007/, (3) year 2008: http://conferences.images.alaska.edu/agu/2008/, (4) year 2009: http://www.snap.uaf.edu/earth/agu/) inthese 4 years covered a wide range of aspects of Earth science applications with vir-tual globes. Most of them were using Google Earth. At the 2010 AGU Fall meeting,there were also many presentations using virtual globes in their applications. A specialissue on ‘Virtual Globes in Science’ – recently published in Computers & Geosciences(Volume 37, Issue 1) – demonstrated many exciting applications. Among all theseGoogle Earth applications, several are focused on large-scale (global, continental,national scale) phenomenona in the atmosphere (Husar et al. 2008, Prados et al. 2010),carbon science (Carr et al. 2009), cryosphere (Ballagh et al. 2007, 2011), ecosystems(Guralnick et al. 2007), energy (Pearce et al. 2007), geology (Yamagishi et al. 2010),health (Aanensen et al. 2009, Chang et al. 2009, Kamadjeu 2009, Lemey et al. 2009,Stensgaard et al. 2009), natural disasters (Nourbakhsh et al. 2006, Yuan et al. 2008,Webley et al. 2009, Turk et al. 2011, Webley 2011), social science (Weidmann and Kuse2009, Leydesdorff and Persson 2010), urban studies (Potere et al. 2009), water (Barkeret al. 2007, Gemmell et al. 2009) etc.


The use of Google Earth in research projects can be summarized into a few gen-eral categories (Goodchild 2008, Stensgaard et al. 2009), such as visualization, datacollection, data exploration, data integration, modelling and simulation, validation,communication/dissemination of research results and decision support.

1. Visualization: A function with a number of purposes depending on whichdata are to be visualized. These data include not only the terrain, remotelysensed images provided by Google Earth itself, but also users’ own vectors,rasters and 3D models overlapped. Multiscale and seamlessly mosaicked Earthobservation images in Google Earth provide comprehensive Earth surfacebackground information. Numerous peer-reviewed papers and reports are nowusing Google Earth for illustrations.

2. Data collection: Usually, spatially explicit data collection requires field tripsequipped with GPS units (or other positioning techniques). It is limited by lackof budget, or difficult access, or lack of GPS units, or even lack of positioningsignals in some locations. Such a data collection process is favoured by imageswith global coverage, such as in Google Earth. But note that the image qualityand the image acquisition data may or may not be suitable for certain researchpurposes at certain locations (see §4.2). For data collection, in most cases, thoseimages provide useful information directly; but for some cases, image process-ing is required (e.g. Bernabe and Plaza 2010, Gong et al. 2010, Guo et al. 2010,Mering et al. 2010).

3. Validation: Similar to data collection, validation using Google Earth imagesshould be conducted with care on the surface object presented in the imagesthat is treated as the real ‘ground truth’. Many land-cover-mapping projectsuse those images as globally distributed ‘ground truth’ (e.g. Potere et al. 2009,Bontemps et al. 2011).

4. Data integration: This refers to the integration of heterogeneous georeferenced1D/2D/3D/4D data in local computers or on the fly from distributed sources.For Google Earth, usually these data are in KML format.

5. Communication and dissemination: These can be referring to the sharing ofgeospatial data/information/knowledge with non-specialists or among scien-tists by using proper visualization.

6. Modelling: Two kinds of modelling need to be distinguished, one is construct-ing static 3D models (such as those models in the ‘3D buildings’ layer in GoogleEarth), another is modelling dynamic phenomena/processes. The latter is themeaning of ‘modelling’ in this article.

7. Data exploration: Exploration of the spatial–temporal patterns of phenomenaby visualizing integrated data at any scale and any angle.

8. Decision support: Supporting decision-making by direct data visualization orthrough integration with other systems via a software interface (e.g. mashup,Global Analyst (http://www.globalanalyst.cn/ga2/index.html)).

For further demonstration, a list of example applications covering multiple (at leasttwo) usages of Google Earth is given in table 1.

3.3 Data sharing in Google Earth: KML data sets

In addition to its direct application in scientific publications, Google Earth isalso being used retrospectively by creating and publishing Google Earth KML


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Table 2. Websites sharing global scientific data set using KML files.

Provider Website

British Geological Survey http://www.bgs.ac.uk/data/services/kml.html

David Tryes’s Google Earth applicationcollection

http://david.tryse.net/googleearth/

DigitalPlanet.Org http://www.digitalplanet.orgGoogle Earth Engine http://earthengine.googlelabs.com/#introGoogle Earth Outreach Showcase http://www.google.com/earth/outreach/

showcase.htmlKing’s College London, Geodata portal http://www.kcl.ac.uk/schools/sspp/

geography/research/emm/geodataJohn Bailey’s earth projects http://snap.uaf.edu/earth/NOAA Tides & Currents http://www.co-ops.nos.noaa.gov/

googleearth.shtmlUK, Foreign & Commonwealth Office, 4◦

scenariohttp://www.fco.gov.uk/en/global-

issues/climate-change/priorities/science/UNEP Shelf Programme http://continentalshelf.org/kmz.aspxUniversity of Colorado at Boulder, National

Snow and Ice Data Center (NSIDC)http://nsidc.org/data/virtual_globes/

USGS Mineral Resources On-Line SpatialData

http://mrdata.usgs.gov/

USGS Earthquake Hazards Program http://earthquake.usgs.gov/learn/kml.php

Note: NOAA, National Oceanic and Atmospheric Administration; USGS, United StatesGeological Survey;

files of key findings to supplement scientific publications and broaden the dis-semination of knowledge (Stensgaard et al. 2009). KML is a primary tool forvisualizing data in 3D/4D in Google Earth. The most recent version of KML(version 2.2) contains many features relevant to scientific data, such as largedata support and the ability to timestamp features and to create animations(http://code.google.com/apis/kml/documentation/). The key strength of Google Earthsoftware is the use of KML to ease the incorporation of data sets from differentproviders and to visualize simultaneously and to identify relationships for subsequentquantitative investigations. Currently, large amounts of geospatial data are now avail-able in KML format. A list of websites sharing the global KML data set is shown intable 2 as examples.

4. Merits and limitations of Google Earth

4.1 Notable features of Google Earth for global scale research

It is beyond the scope of this article to describe all features of Google Earth.The full documentation and tutorials and other materials are available at http://earth.google.com. However, several notable merits of Google Earth for global scaleresearch are elaborated below.

1. The strength of Google Earth is the use of a single coordinate system, that is,geographic coordinates (latitude/longitude) on the World Geodetic System of1984 (WGS84) datum. All geospatial data are stored based on a spherical Earthmodel rather than in various types of projected 2D systems. This means thatthe user can establish a perspective view of map/image layers with only one


projection necessary at the display. Multisource data sets in KML format canbe easily integrated and shared on the sphere.

2. Easy visualization is another significant merit. Google Earth allows simultane-ous access to various types of data, making it well-suited for the ‘exploratory’phase of scientific work (Oberlies et al. 2009, Stensgaard et al. 2009). GoogleEarth provides a number of features to facilitate vertical geospatial data visu-alization (Geens 2006, Yamagishi et al. 2006, Chen et al. 2009, De Paor andWhitmeyer 2011), which is helpful for exploring relationships among quantita-tive data in three dimensions. Recent vertical rendering applications in GoogleEarth used the COLLADA model mostly, for example rendering above-surfaceatmospheric conditions (Chen et al. 2009) and under-surface geological set-tings (De Paor and Whitmeyer 2011). This powerful and easy-to-use 3Dvisualization functionality for heterogeneous data sets can facilitate globalscale research in (1) exploring relationships among quantitative data (e.g.Butler 2006, Webley 2011) and (2) presenting outcomes of model predictions(e.g. Hai et al. 2010). This is helpful to users in generating scientific hypothe-ses from a global viewpoint. Especially, this vision becomes clear and concretewhen Google Earth supports spatial–temporal visualization (Compieta et al.2007, Wood et al. 2007), for example, volcanic ash dispersion (Webley et al.2009) and person-based accessibility of urban opportunities (Neutens et al.2010).

3. Freely accessible remotely sensed images in Google Earth facilitate (providingbase map and/or validation) research in urban studies, disaster relief, natu-ral resource management and so on. An evaluation of horizontal positionalaccuracies for Google Earth’s images is 40 m RMSE (root mean square error)calculated from 436 points chosen worldwide (Potere 2008). The accuracy ofcontrol points in more developed countries is 24.1 m RMSE, and 44.4 mRMSE in developing countries (Potere 2008). A local investigation conductedby comparing virtually traced positions in Google Earth against high-precision(<1 m) field measurements along three stratigraphic unconformity sub-sectionsin Texas, USA, shows much better than the accuracies of the terrain model inGoogle Earth – 2.64 m RMSE horizontal and 1.63 m RMSE vertical (Benkeret al. 2011). These plausibly indicate that high-resolution imagery and the ter-rain model in Google Earth have sufficient horizontal positional accuracy forassessing moderate-resolution RS products across most of the world’s urbanareas. It also indicates that high-resolution images in Google Earth are suffi-cient for site validation, which is a difficult-to-accomplish requirement in globalmapping.

4.2 Limitations of Google Earth

Google Earth is not perfect for scientific applications, especially for large-scaleresearch. There exist several limitations (applicable to most other virtual globes),that is the inconsistent quality of images, insufficient capability for quantitative mea-surement, lack of analytical functionalities, lack of support for precise global spatialsimulation due to its use of variable longitude/latitude grids, security issues, etc.

1. The inconsistency of remotely sensed image quality in Google Earth impedesconsistent analysis. Specifically:


• Position accuracies presented in §4.1 are not always available on the wholeglobe of Google Earth. Presently, high-resolution images in Google Earthcover about 20–30% of the world (Stensgaard et al. 2009). That means mostareas in the world suffer from poor coverage of high-resolution imagery.Besides, Google Earth uses at least 30 m terrain data for the USA but 90 mfor most of the rest of the world. Therefore, in areas of extreme topography,images may contain greater distortion error. Although Google continuallyupdates the resolution of overlapped image and terrain data, it is far frommeeting the demands of many users. In this case, one should exercise extremecaution when using Google Earth. Discouragingly, a recent evaluation onthe horizontal position accuracy of Google Earth imagery using the loca-tion information of 2045 runways distributed worldwide shows the meandisparity is 113 m, while the maximum is 1676 m (Becek et al. 2011).

• Inconsistent acquisition date and different temporal frequencies. Despite thebase maps in Google Earth (RS images, roads, administrative units, topog-raphy, etc.) being extensive, the updating frequency does not meet thefrequency requirement by certain applications, for example, annual land-cover-change analysis.

2. Precise quantitative measurements and analysis is lost in Google Earth.Specifically:• Images from Google Earth are processed for visualization purposes (images

are intended for optimized ground cover presentation) and are not suit-able for some analyses because (1) different multispectral combinations ofremotely sensed images are not supported; (2) there is no temporal flexibilityof image acquisition (Monkkonen 2008, Kamadjeu 2009); and (3) thereis largely inconsistent radiometric distortion – thus quantitative values ofbiogeophysical parameters cannot be extracted.

• Quantitative measurements of topographic parameters (areas, slopes) arenot possible without recourse to additional software and digital data sets,for example, digital elevation models (DEMs) (Tooth 2006). Existing virtualglobes use a fixed global terrain model. It is either hard or impossible toimport custom high-resolution topographic data such as LiDAR DEMs intothem (Bernardin et al. 2011).

• Analysis utilities are inadequate. At present, Google Earth appears to be pri-marily used as a geobrowser for exploring spatially referenced data. GoogleEarth until now supports only a small portion of what a full GIS softwarepackage does in applications. Google Earth’s functionalities need to be inte-grated with analytical tools for spatial analysis while facilitating the sharingof spatially referenced data among its users.

3. The representation and tessellation of the Earth’s surface in Google Earthcontain inadequate operations. Specifically:• The internal coordinate system of Google Earth is geographic coordinates

(latitude/longitude) based on the WGS84 datum. Google Earth uses thepseudo-elliptical WGS84 model – a sphere-based geographic coordinate sys-tem called ‘WGS 1984 Major Auxiliary Sphere’. Wrapping images on such asimple model of the Earth’s shape will introduce additional horizontal andvertical location errors.

• Limitations of the equal angle latitude/longitude grid system have been rec-ognized for ages. Simply speaking, grids not equal in area, polar singularities


and lack of uniform adjacency are the common deficiencies (Sahr and White1999). Studies on global climate modelling, for example, used other types ofgrid systems with quasi-homogeneous grids over the sphere to overcome thepolar problem in simulation (Satoh et al. 2008). The tessellation fashion (forboth images and terrain models) in Google Earth needs to be improved ifmore serious Earth simulation application is to be made.

4. The usefulness of KML or KMZ (KML files when compressed) in effectivedata sharing and visualization is obvious. However, risks need to be consid-ered before applying these techniques in areas of public interest (Sheppard andCizek 2009), including displaying traceable health information in the publicspace and concerns with confidentiality of the data (Curtis et al. 2006). Notethat most of these shortcomings are not unique to Google Earth; they are appli-cable to almost the entire geospatial industry. Compared to NASA World Wind(open source) and ArcGIS Explorer (backup by ArcGIS Desktop/Server), aunique shortcoming of Google Earth is its lack of extendibility.

5. Improving functionalities in virtual globes for global scale applications

One unique advantage of virtual globes is their capability to manage data with globalcoverage. This unique feature has been largely ignored other than rendering globalviews. Undoubtedly, global scale information processing, analysis and simulationtasks can take advantage of virtual globes. Some efforts have been made to increasethe functionalities in virtual globes for global scale applications. They can be groupedin several directions.

5.1 Towards global mapping: creating a custom database to provide credible andsufficient data sets

To solve the data inconsistency problem, a conventional approach is for a user tobuild his or her own database with credible data sets (an additional metafile statingthe image-processing procedures and uncertainty issues is preferred). Such a databasecan be built by integrating on-site measurements, satellite imagery, aerial photographyand digital map data which require further processing (Chen et al. 2008) and/or for-mat conversions (Yamagishi et al. 2006, 2010, Oberlies et al. 2009). Yamagishi et al.(2006) adopted Google Earth as the visualization platform and developed conver-sion tools (called ‘KML generators’) to help display various geoscience data. Theysuccessfully generated KML files for seismic tomographic models (Yamagishi et al.2010), geochemical data of rocks (Yamagishi et al. 2006) and geomagnetic field models(Nagao et al. 2008). Oberlies et al. (2009) developed an open source program to con-vert three different types of data: (1) coordinate data stored in GPS devices (e.g.Garmin, Magellan, Trimble and others) or in waypoint management software; (2)data in spreadsheets (Excel or similar); and (3) data stored in relational databases(Access or similar) to CSV (comma separated value), then to KML. With thesetools, an overlapping visual presentation of different types of data can be obtained.Existing KML files of geoscience data (e.g. KMLs listed in table 2) provided byvarious research institutes around the world can also be overlaid on these data to com-pose a database. Many virtual globe-based databases are now available, for example,Dagik (Data-showcase system for Geoscience in KML, http://dagik.org/) and Dapple(http://dapple.geosoft.com/). Most recently, Google unveiled a new online product –Google Earth Engine (http://earthengine.googlelabs.com) – which makes trillions of


quality-trusted satellite images over the world (dating back to more than 25 years ago)available.

5.2 Towards global analysis: extending analytical functionality by KML, web servicesand API/SDK

An approach to improving the analysis capability of Google Earth is taking outputsfrom analysis tools to do visualization. New tools and add-ons to existing softwarepackages for spatial analysis are being increasingly developed to export their anal-ysis results to KML files (table 3). For example, users of ESRI’s ArcGIS products(version 9.2 and onwards) and ITT ENVI (version 4.6 and onwards) can now exporttheir vector and raster layers directly into KML format. Many practical guides onhow to integrate analytical toolbox with Google Earth through KML are available atwww.spatial-analyst.net and the book by Hengl (2009). It is a promising approach toextend in this way. As an example, the R+SAGA/GRASS+GE can cover practically80% of processing/visualization capabilities available in ArcInfo/Map or Idrisi (Hengl2009).

Another way to equip virtual globes with analytic functions is integration withWeb services (Gong et al. 2009, Renner et al. 2009). The integration combines thevisualization and communication power of virtual globes with powerful analysis func-tionalities of geospatial web services to help researchers investigate various scientificproblems in an environment with natural and intuitive user experiences. This integra-tion will provide analysis-enhanced virtual globes for scientific research. These webservice technologies, especially those standard-based interoperable geospatial services(namely OGC, Web Map Service (WMS), Web Feature Service (WFS), Web CoverageService (WCS), Web Processing Service (WPS), Catalogue Service for the Web (CSW)),make a large amount of geoprocessing functionalities easily accessible to researchersas if they were from their local resources. Take ArcGIS Explorer as an example. ESRIhave developed numerous sophisticated spatial analysis and map editing functions,many of which are served as geoprocessing web services via ArcGIS Server. Therefore,by connecting to ArcGIS Server, ArcGIS Explorer is no longer a visualization toollike Google Earth, but equipped with a series of analytic tools.

The current version of ArcGIS Explorer supports two ways to implement spa-tial analysis: (1) creating add-ins using Visual Studio and the ArcGIS ExplorerSDK (Kienberger and Tiede 2008) and (2) using Analysis Gallery (ArcGIS ExplorerResource Center 2010) to integrate with the web services provider ArcGIS Server.Although the first way is not different to other virtual globes (e.g. NASA WorldWind Java SDK, Google Earth Application Programming Interface (API)), the sec-ond makes ArcGIS Explorer most outstanding because it is backed by the largest

Table 3. Examples of toolboxes for exporting analysis results from different platforms to KMLformat.

Platform Developer Website

FORTRAN Chiang et al. (2007) http://web.me.com/dove_family/xml/fox.htmlMATLAB Skawtus and Spaaks

(2006)http://code.google.com/p/googleearthtoolbox/

R Language Temple Lang (2010) http://www.omegahat.org/RKML/R Language Lwein-Koh and Bivand

(2011)http://cran.r-project.org/web/packages/

maptools/index.html


GIS industry. By using Analysis Gallery, scientists can run models and simultaneouslyview them over the Internet in ArcGIS Explorer by dragging and dropping data fileswithout coding. Fortunately, those analysis web services provided by ArcGIS Servercan also be used to extend capabilities of geospatial analysis by other virtual globes.

Another example of extending analytical functionality in Google Earth is GlobalAnalyst (http://www.globalanalyst.cn/ga2/index.html). A spherical coordinate systemcomplied GIS – Global Analyst (GA) has been developed for global scale spatial anal-ysis on Google Earth. GA integrates functions from two different systems (GIS andRS) and adopts advanced software technologies such as cloud computing, scalablearchitecture and multilingual hybrid programming. In GA, globally distributed mul-tisource, multiscale, multitemporal geospatial (e.g. shp., tif.) data and services (e.g.WMS, WCS, WFS) can be aggregated or mashup. Customized functions for particularapplications can be added into GA with JavaScript (Gong et al. 2011).

Besides, the newly released product, Google Earth Engine, allows processing,analysing and interpreting such remotely sensed imagery with high-performancetools based on parallel processing platforms. Although limited instances are availablecurrently, this is a promising tool for scientific applications in global scale studies.

5.3 Towards global simulation: better discrete global grid systems

The inadequacies of discrete global grids (DGGs) based on the latitude/longitude gridhas led a number of researchers to explore alternative approaches. As has been widelyobserved, the spherical versions of the five platonic solids (namely the tetrahedron,cube, octahedron, dodecahedron and icosahedron) represent the only ways in whichthe sphere can be partitioned into cells each consisting of the same regular sphericalpolygon, with the same number of polygons meeting at each vertex (White et al. 1992,Sahr and White 1999). These new DGGs are useful for large-scale, especially trulyglobal, data processing (Kiester and Sahr 2008, White and Kiester 2008). PYXIS(http://www.pyxisinnovation.com/derm.php) is an example of an icosahedron-basedDGG virtual globe. These new DGGs possess unique features such as (1) uniformpresentation (to both polar and non-polar regions) over the entire globe at any resolu-tion, (2) uniform adjacent topology and (3) equal area in each cell. These are especiallyattractive properties for bridging local simulations with global simulations.

These new DGGs are also helpful to refine the topographic resolution, a short-coming in Google Earth as mentioned in §4.2, a different DGG-based alternativevirtual globe called Crusta, which is well-suited to virtual geologic investigation ofhigh-resolution (sub-metre) topographical data on Earth (Bernardin et al. 2011).Different from Google Earth, in Crusta, data of interest are preprocessed into an opti-mized, tiled and multiscaled global representation on the surface of the geoid throughsubdivision of a 30-sided base polyhedron. Using geoid rather than the sphere repre-sentation in virtual globes will definitely improve the position accuracy of overlappedremotely sensed images and terrain models and benefit studies of global change (e.g.sea-level change, ocean circulation, Earth interior processes).

5.4 Towards an environment for secure global mapping, analysis and simulation

A straightforward way to protect and have control of geospatial data in GoogleEarth is to use it off-line or by creating a customized Google Earth. TheGoogle Earth Enterprise Version (http://www.google.com/enterprise/earthmaps/earth_enterprise.html) provides freedom in such a manner and a capability to limit


access to specific groups or individuals. At a higher level, two main conceptualapproaches to provide safeguards or limits on threats to visualization quality havebeen defined (Sheppard 2005): (1) more prescriptive approaches which guide ordrive the presentation of visualization material according to shared principles orstandards; and (2) more flexible and interactive approaches which give much greatercontrol over visualization information to the user/viewer. Both approaches providegreater access to and transparency of visual imagery and underlying metadata thanis normal with current tools, though in different ways and to differing degrees. Anumber of possible solutions to enhance visualization validity and reliability, usingone or both of these approaches, are briefly outlined in Sheppard and Cizek (2009).With regards to the copyright issues, Google, as an example, released permissionguides for using geospatial information from Google Earth and Google Maps(http://www.google.com/permissions/geoguidelines.html). For a single KML/KMZfile provided by institution or individual suppliers, adding appropriate copyright text(or copyright watermark for images) into content as comment is recommended.

6. Conclusions and outlook

The rapidly growing field of 3D mapping and modelling of the Earth holds promisefor applications at the global scale. Easy-to-use and intuitive virtual globe technologiessuch as Google Earth have obvious advantages over conventional GIS: (1) breakingthe old mindset of using and developing geospatial information products offers newconcepts (seamless, no scale, no secret); (2) combining the idea of serving the publicwith advanced technology opens a broad application space of geospatial informationtechnology; (3) integrating huge volumes of data in widely distributed geospatial dataservers with mature search technology lays an infrastructure for distributed spatialinformation systems; and (4) adopting data exchange and interoperability specifica-tions allows the unification of heterogeneous systems and heterogeneous data in oneplatform.

However, at the present stage, virtual globes can be literally defined as‘geobrowsers’ – an alternative web-browsing interface and paradigm. Compared tostandard internet browsers, for example, Microsoft Internet Explorer (MSIE), GoogleEarth as an outstanding representative of virtual globe is similar in software size(latest version 6 of Google Earth is about 13 megabytes MS IE version 8 is about16 megabytes), but much richer in presentation of Earth information. Virtual globescan be used in a visually rich and location-specific way to browse for content; andalmost any type of data (including video) can be rendered inside Google Earth.However, Google Earth as well as other virtual globes suffers from meeting therequirement of global scale studies including global mapping, analysis and simu-lation. As summarized by Craglia et al. (2008), the recent developments of mostsuccessful geobrowsers are led by commercial companies targeted for a mass marketaudience, and the key drivers are market share and advertising revenue, rather thanto advance research. Therefore, scientists should not only take full advantage of thosefast-growing commercial platforms, but also develop their own techniques in order toimprove global scale scientific research.

Although many parts of the vision of Digital Earth have been realized (evidencedin part by the popularity of virtual globe geobrowsers for commercial, social and sci-entific applications), a truly global, collaborative link of systems, as outlined in Gore’sspeech (1998), has yet to happen (Craglia et al. 2008, Foresman 2008, Goodchild


2008). There is a greater recognition of the need for web-based geographic com-puting worldwide, which requires the virtual globe to become a truly Digital Earthfor global scale research. This process can be generally identified in two aspects:one is developing a global and collaborative social community, a virtual world(or a computer-simulated ‘social’ world), and the other is developing global andcollaborative Earth testbeds – an Earth laboratory (or a computer-simulated ‘nature’Earth). Both of them may share the same technologies, the same user experiences andeven the same users (both the general public and scientific community).

The current generation of virtual globes has already established an interactive3D/4D virtual environment that enables scientists to conduct their research with aglobal perspective in a more natural and intuitive way. Future developments shouldaim at making virtual globes even easier to use, practical and efficient research tools.Future virtual globes should extend beyond geobrowsing to include all informationextraction and analysis capabilities at the local and global scales. Finally, it is desirableto have systems that empower the user with such functions as automatic generation ofhigh standard artworks, that is tables, plots, locations maps, figures and multimedia(e.g. audio, video if necessary) and statistics reports that can be extracted into scientificpapers and reports directly.

AcknowledgementsThis research is partially supported by the National Science Foundation (NSF)Biocomplexity Grant (grant no. NSF DEB 04-21530) and the National HighTechnology Programme of China (grant no. 2009AA12200101).

ReferencesAANENSEN, D.A., HUNTLEY, D.M., FEIL, E.J., AL-OWN, F. and SPRATT, B.G., 2009,

EpiCollect: linking smartphone to Web applications for epidemiology, ecology andcommunity data collection. PLOS One, 4, pp. 1–7.

AL-SABHAN, W., MULLIGAN, M. and BLACKBURN, G.A., 2003, A real-time hydrological modelfor flood prediction using GIS and WWW. Computers, Environment and Urban Systems,27, pp. 9–32.

ARCGIS EXPLORER RESOURCE CENTER, 2010. Available online at: http://webhelp.esri.com/arcgisexplorer/1500//en/index.html

BAILEY, J.E. and CHEN, A., 2011, The role of virtual globes in geoscience. Computers &Geosciences, 37, pp. 1–2.

BALLAGH, L.M., PARSONS, M.A. and SWICK, R., 2007, Visualising cryospheric images in avirtual environment: present challenges and future implications. Polar Record, 43, pp.305–310.

BALLAGH, L.M., RAUP, B.H., DUERR, R.E., KHALSA, S.J.S., HELM, C., FOWLER, D. andGUPTE, A., 2011, Representing scientific data sets in KML: methods and challenges.Computers & Geosciences, 37, pp. 57–64.

BARKER, S., ROBARTS, R., YAMASHIKI, Y., TAKEUCHI, K., YOSHIMURA, C. and MUGUETTI,A.C., 2007, UNEP-GEMS/Water Program – water quality data, GEMStat and openweb services – and Japanese cooperation. Hydrological Processes, 21, pp. 1132–1141.

BAR-ZEEV, A., 2007, How Google Earth [Really] Works. Available online at:http://www.realityprime.com/articles/how-google-earth-really-works

BEAUDETTE, D.E. and O’GEEN, A.T., 2009, Soil-Web: an online survey for California, Arizona,and Nevada. Computers & Geosciences, 35, pp. 2119–2128.

BECEK, K., IBRAHIM, K. and BARUSSALAM, B., 2011, On the positional accuracy of theGoogle Earth imagery. In FIG Working Week 2011, Bridging the Gap Between Cultures,Working Paper No. 4947, 18–22 May, Marrakech, Morocco.


BENKER, S.C., LANGFORD, R.P. and PAVILIS, T.L., 2011, Positional accuracy of the GoogleEarth terrain model derived from stratigraphic unconformities in the Big Bend region,Texas, USA. Geocarto International, 26, pp. 1–13.

BERNABE, S. and PLAZA, A., 2010, A new system to perform unsupervised and supervisedclassification of satellite images from Google Maps. In Satellite Data Compression,Communications, and Processing VI , Proceedings of SPIE, 3 August 2010 (Bellingham,WA: SPIE), Vol. 7810, pp.1–10.

BERNARDIN, T., COWGILL, E., KREYLOS, O., BOWLES, C., GOLD, P., HAMANN, B. andKELLOGG, L., 2011, Crusta: a new virtual globe for real-time visualization of sub-meterdigital topography at planetary scales. Computers & Geosciences, 37, pp. 75–85.

BINDER, J., 2005, Earth to Google: satellite images on your screen. Aerospace America, 11, pp.18–20.

BONTEMPS, S., DEFOURNY, P., BOGAERT, E.V., ARINO, O., KALOGIROU, V. and REREZ, J.E.,2011, GLOBCOVER 2009 – products description and validation reports. Availableonline at: http://ionia1.esrin.esa.int/docs/GLOBCOVER2009_Validation_Report_2.2.pdf

BOULOS, M., 2005, Web GIS in practice III: creating a simple interactive map of England’sstrategic health authorities using Google Maps API, Google Earth KML, andMSN Virtual Earth Map Control. International Journal of Health Geographics, 4,p. 22.

BOYD, D.S. and FOODY, G.M., 2011, An overview of recent remote sensing and GIS basedresearch in ecological informatics. Ecological Informatics, 6, pp. 25–36.

BUTLER, D., 2006, Mashups mix data into global service. Nature, 439, pp. 6–7.CARR, T.R., IQBAL, A., CALLAGHAN, N., ADKINS-HELJESON, D., LOOK, K., SAVING, S. and

NELSON, K., 2009, A national look at carbon capture and storage – national car-bon sequestration database and geographical information system (NatCarb) . EnergyProcedia, 1, pp. 2841–2847.

CHANG, A.Y., PARRALES, M.E., JIMENEZ, J., SOBIESZCZYK, M.E., HAMMER, S.M.,COPENHAVER, D.J. and KULKARNI, R.P., 2009, Combining Google Earth and GIS map-ping technologies in a dengue surveillance system for developing countries. InternationalJournal of Health Geographics, 8, pp. 1–11.

CHEN, A., LEPTOUKH, G., KEMPLER, S., LYNNES, C., SAVTCHENKO, A., NADEAU, D. andFARLEY, J., 2009, Visualization of A-Train vertical profiles using Google Earth.Computers & Geosciences, 35, pp. 419–427.

CHEN, A., LEPTOUKH, G., KEMPLER, S., NADEAU, D. and ZHANG, Z., 2008, Augmenting theresearch value of geospatial data using Google Earth. In Google Earth Science Journal ofthe Virtual Explorer, D. De Paor (Ed.), electronic edition, vol. 29, 100 p. Available onlineat: http://virtualexplorer.com.au/article/2008/194/geospatial-data-using-google-earth

CHIANG, G.-T., WHITE, T.O.H. and DOVE, M.T., 2007, Driving Google Earth from Fortran.Available online at: http://www.allhands.org.uk/2007/proceedings/papers/825.pdf

COMPIETA, P., MARTINO, S.D., BERTOLOTTO, M., FERRUCCI, F. and KECHADI, T., 2007,Exploratory spatio-temporal data mining and visualization. Journal of VisualLanguages & Computing, 18, pp. 255–279.

CONROY, G.C., ANEMONE, R.L., REGENMORTER, J.V. and ADDISON, A., 2008, Google Earth,GIS and the Great Divide: a new and simple method for sharing paleontological data.Journal of Human Evolution, 55, pp. 751–755.

CRAGLIA, M., GOODCHILD, M.F., ANNONI, A., CAMARA, G., GOULD, M., KUHN, W., MARK,D., MASSER, I., MAGUIRE, D., LINAG, S. and PARSONS, E.D., 2008, Next-generationdigital Earth – a position paper from the Vespucci Initiative for the Advancement ofGeographic Information Science. International Journal of Spatial Data InfrastructuresResearch, 3, pp. 146–167.

CURTIS, A.J., MILLS, J.W. and LEITNER, M., 2006, Spatial confidentiality and GIS:re-engineering mortality locations from published maps about Hurricane Katrina.International Journal of Health Geographics, 5, p. 44.


DE PAOR, D.G. and WHITMEYER, S.J., 2011, Geological and geophysical modeling on vir-tual globes using KML, COLLADA and JavaScript. Computers & Geosciences, 37,pp. 100–110.

FORESMAN, T.W., 2008, Evolution and implementation of the Digital Earth vision, technologyand society. International Journal of Digital Earth, 1, pp. 4–16.

GEENS, S., 2006, Proof of concept: visualizing vertical data in Google Earth. Available onlineat: http://www.ogleearth.com/2006/07/proof_of_concep.html

GEMMELL, A.L., SMITH, G.C., HAINES, K. and BLOWER, J.D., 2009, Validation of ocean modelsyntheses against hydrography using a new web application. Journal of OperationalOceanography, 2, pp. 29–41.

GONG, J., YUE, P., XIANG, L. and CHEN, J., 2009, Integrating virtual globes and web ser-vice technologies for higher-education teaching and research. In Proceedings of the2009 ISPRS International Workshop on Virtual Changing Globe for Visualisation andAnalysis (VCGVA 2009), 27–28 October 2009, Wuhan, China, Vol. XXXVIII- 4/W10,pp. 1682–1777, ISSN No.:1682–1777.

GONG, P., 2007, Wireless sensor network as a new ground remote sensing technology forenvironmental monitoring. Journal of Remote Sensing, 11, pp. 545–551.

GONG, P., LI, Z., HUANG, H., SUN, G. and WANG, L., 2010, ICEsat GLAS data for urbanenvironment monitoring. IEEE Transactions on Geoscience and Remote Sensing, 49,pp. 1158–1172.

GONG, P., ZHAO, Y.C., YU, L. and LIANG, L., 2011, Development of an integrated softwareplatform for global mapping and analysis. Geomatics World, 2, pp. 34–37.

GOODCHILD, M.F., 2008, The use cases of digital earth. International Journal of Digital Earth,1, pp. 31–42.

GORE, A., 1992, Earth in the Balance: Ecology and Human Spirit (Boston, MA: HoughtonMifflin).

GORE, A., 1998, The digital earth: understanding our planet in the 21 century. Available onlineat: http://portal.opengeospatial.org/files/?artifact_id=6210

GUO, J.H., LIANG, L. and GONG, P., 2010, Removing shadows from Google Earth images.International Journal of Remote Sensing, 31, pp. 1379–1389.

GURALNICK, R.P., HILL, A.W. and LANE, M., 2007, Towards a collaborative, global infrastruc-ture for biodiversity assessment. Ecology Letters, 10, pp. 663–672.

HAI, P.T., MAGOME, J., YOROZUYA, A., INOMATA, H., FUKAMI, K. and TAKEUCHI, K., 2010,Large-scale flooding analysis in the suburbs of Tokyo Metropolis caused by levee breachof the Tone River using a 2D hydrodynamic model. Water Science and Technology, 62,pp. 1859–1864.

HENGL, T., 2009, A Practical Guide to Geostatistical Mapping, 2nd ed. (Amsterdam: Universityof Amsterdam), 291 p. Available online at: www.lulu.com

HEYMAN, J., 2005, The ants go marching – on your screen. The Scientist, 19, p. 24.HU, H.T., GONG, P. and XU, B., 2010, Spatially explicit agent-based modelling for schistoso-

miasis transmission: human-environment interaction simulation and control strategyassessment. Epidemics, 2, pp. 49–65.

HUSAR, R.B., HOIJARVI, K., FALKE, S.R., ROBINSON, E.M. and PERCIVALL, G.S., 2008,DataFed: an architecture for federating atmospheric data for GEOSS. IEEE SystemsJournal, 2, pp. 366–373.

KAMADJEU, R., 2009, Tracking the polio virus down the Congo River: a case study on the useof Google Earth in public health planning and mapping. International Journal of HealthGeographics, 8, p. 4.

KIENBERGER, S. and TIEDE, D., 2008, Is this yet another virtual globe? ArcGIS Explorer review.Geoinformatics, 11, pp. 42–47.

KIESTER, A.R. and SAHR, K., 2008, Planar and spherical hierarchical, multi-resolution cellularautomata. Computers, Environment and Urban Systems, 32, pp. 204–213.

LEEMANS, R., ASRAR, G., BUSALACCHI, A., CANADELL, J., INGRAM, J., LARIGAUDERIE, A.,MOONEY, H., NOBRE, C., PATWARDHAN, A., RICE, M., SCHMIDT, F., SEITZINGER,


S., VIRJI, H., VOROSMARTY, C. and YOUNG, O., 2009, Developing a common strat-egy for integrative global environmental change research and outreach: the EarthSystem Science Partnership (ESSP). Current Opinion in Environmental Sustainability, 1,pp. 4–13.

LEMEY, P., RAMBAUT, A., DRUMMOND, A.J. and SUCHARD, M.A., 2009, Bayesian phylogeog-raphy finds its roots. PLOS Computational Biology, 5, e1000520.

LEYDESDORFF, L. and PERSSON, O., 2010, Mapping the geography of science: distributionpatterns and networks of relations among cities and instates. Journal of the AmericanSociety for Information, 61, pp. 1622–1634.

LIANG, L., XU, B., CHEN, Y.L., LIU, Y., CAO, W.C., FANG, L.Q., FENG, L.M., GOODCHILD,M.F. and GONG, P., 2010, Combining spatial-temporal and phylogenetic analysisapproaches for improved understanding on global H5N1 transmission. PLoS ONE,5, p. e13575.

LONGLEY, P., GOODCHILD, M., MAGUIRE, D. and RHIND, D., 2001, Geographic InformationSystems and Science (New York: Wiley).

LOZANO-FUENTES, S., ELIZONDO-QUIROGA, D., FARFAN-ALE, J.A., GARCIA-REJON, J., LIRA-ZUMBARDO, V., NAJERA-VAZQUEZ, R., FERNANDEZ-SALAS, I., CALDERON-MARTINEZ,J., DOMINGUEZ-GALERA, M., MIS-AVILA, P., MORRIS, N., COLEMAN, M., BEATYA, B.J.and EISENA, L., 2008, Use of Google Earth to strengthen public health capacity andfacilitate management of vector-borne diseases in resource-poor environments. Bulletinof the World Health Organization, 86, pp. 718–725.

LWEIN-KOH, N.J. and BIVAND, R., 2011, Maptools: tools for reading and handlingspatial objects. Available online at: http://cran.r-project.org/web/packages/maptools/index.html

MALCZEWSKI, J., 2004, GIS-based land-use suitability analysis: a critical overview. Progress inPlanning, 62, pp. 3–65.

MERING, C., BARO, J. and UPEGUI, E., 2010, Retrieving urban areas on Google Earth images:application to towns of West Africa. International Journal of Remote Sensing, 31,pp. 5867–5877.

MONKKONEN, P., 2008, Using online satellite imagery as a research tool – mapping changingpatterns of urbanization in Mexico. Journal of Planning Education and Research, 28,pp. 225–236.

NAGAO, H., YAMAGISHI, Y., TSUBOI, S., YANAKA, H. and HATAKEYAMA, T., 2008, KML gen-erator to visualize geomagnetic field models on Google Earth. EOS Transaction ofAmerican Geophysical Union, 89, p. 53.

NEUTENS, T., VERSICHELE, M. and SCHWANEN, T., 2010, Arranging place and time: a GIStoolkit to assess person-based accessibility of urban opportunities. Applied Geography,30, pp. 561–575.

NOURBAKHSH, I., SARGENT, R., WRIGHT, A., CRAMER, K., MCCLENDON, B. and JONES, M.,2006, Mapping disaster zones. Nature, 439, pp. 787–788.

OBERLIES, N.H., RINEER, J.I., ALALI, F.Q., TAWAHA, K., FALKINHAM III, J.O. and WHEATON,W.D., 2009, Mapping of sample collection data: GIS tools for the natural productresearcher. Phytochemistry Letters, 2, pp. 1–9.

PEARCE, J.M., JOHNSON, S.J. and GRANT., G.B., 2007, 3D-mapping optimization of embodiedenergy of transportation. Resources, Conservation and Recycling, 51, pp. 435–453.

POTERE, D., 2008, Horizontal positional accuracy of Google Earth’s high-resolution imageryarchive. Sensors, 8, pp. 7973–7981.

POTERE, D., SCHNEIDER, A., ANGEL, S. and CIVCO, D., 2009, Mapping urban areas on a globalscale: which of the eight maps now available is more accurate? International Journal ofRemote Sensing, 30, pp. 6531–6558.

PRADOS, A.I., LEPTOUKH, G., LYNNES, C., JOHNSON, J., RUI, H., CHEN, A. and HUSAR, R.,2010, Access, visualization, and interoperability of air quality remote sensing datasets via the Giovanni Online Tool. IEEE Journal of Selected Topics in Applied EarthObservations and Remote Sensing, 3, pp. 359–370.


PRINGLE, H., 2010, Google Earth shows clandestine worlds. Science, 329, pp. 1008–1009.REID, W.V., CHEN, D.L., GOLDFARB, L., HACKMANN, H., LEE, Y.T., MOKHELE, K., OSTROM,

E., RAIVIO, K., ROCKSTRÖM, J., SCHELLNHUBER, H.J. and WHYTE, A., 2010, Earthsystem science for global sustainability: grand challenges. Science, 330, pp. 916–917.

RENNER, R.D., HEMANI, Z.Z. and TJOUMAN, G.C., 2009, Extending advanced geospatialanalysis capabilities to popular visualization tools. Technology Review Journal, 17,pp. 89–106.

RINALDI, L., MUSELLA, V., BIGGERI, A. and CRINGOLI, G., 2006, New insights into the applica-tion of geographical information systems and remote sensing in veterinary parasitology.Geospatial Health, 1, pp. 33–47.

SAHR, K. and WHITE, D., 1999, Discrete global grid system. Computing Science and Statistics,30, pp. 269–278.

SAITO, A. and YOSHIDA, D., 2009, Dagik: a data-showcase system for the geospace. DataScience Journal, 8, pp. S92–S95.

SATOH, M., MATSUNO, T., TOMITA, H., MIURA, H., NASUNO, T. and IGA, S., 2008,Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolvingsimulations. Journal of Computational Physics, 227, pp. 3486–3514.

SCHONING, J., HECHT, B., RAUBAL, M., KRUGER, A., MARSH, M. and ROHS, M., 2008,Improving interaction with Virtual Globes through spatial thinking: helping users ask‘why?’. In IUI’08, Proceedings of the 13th International Conference on Intelligent UserInterfaces, 13–16 January, Maspalomas, Gran Canaria, Spain.

SHAPIRO, M., SHUKLA, J., BRUNET, G., NOBRE, C., BÉLAND, M., DOLE, R., TRENBERTH, K.,ANTHES, R., ASRAR, G., BARRIE, L., BOUGEAULT, P., BRASSEUR, G., BURRIDGE, D.,BUSALACCHI, A., CAUGHEY, J., CHEN, D., CHURCH, J., ENOMOTO, T., HOSKINS, B.,HOV, ∅., LAING, A., LE TREUT, H., MAROTZKE, J., MCBEAN, G., MEEHL, G., MILLER,M., MILLS, B., MITCHEL, J., MONCRIEFF, M., NAKAZAWA, T., OLAFSSON, H., PALMER,T., PARSONS, D., ROGERS, D., SIMMONS, A., TROCCOLI, A., TOTH, Z., LOUIS, U.,VELDEN, C. and WALLACE, J.M., 2010, An earth system prediction initiative for the21st century. Bulletin of the American Meteorological Society, 91, pp. 1377–1388.

SHEPPARD, S.R.J., 2005, Validity, reliability, and ethics in visualization. In Visualization inLandscape and Environmental Planning: Technology and Applications, I. Bishop and E.Lange (Eds.), pp. 79–97 (London: Taylor and Francis).

SHEPPARD, S.R.J. and CIZEK, P., 2009, The ethics of Google Earth: crossing thresholds fromspatial data to landscape visualization. Journal of Environmental Management, 90,pp. 2012–2117.

SKAWTUS, E. and SPAAKS, J.H., 2006, Google Earth Toolbox. Available online at:http://code.google.com/p/googleearthtoolbox/

SMITH, T.M. and LAKSHMANAN, V., 2011, Real-time, rapidly updating severe weather productsfor virtual globes. Computers & Geosciences, 37, pp. 3–12.

STENSGAARD, A., SAARNAK, C.F.L., UTZINGER, J., VOUNATSOU, P., SIMOONGA, C.,MUSHINGE, G., RAHBEK, C., MOHLENBERG, F. and KRISTENSEN, T.K., 2009, Virtualglobes and geospatial health: the potential of new tools in the management and controlof vector-borne diseases. Geospatial Health, 3, pp. 127–144.

STRANGEWAYS, I., 2009, Using Google Earth to evaluate GCOS weather station sites. Weather,64, pp. 4–8.

SZEWCZYK, R., OSTERWEIL, E., POLASTRE, J., HAMILTON, M., MAINWARRING, A. and ESTRIN,D., 2004, Habitat monitoring by sensor network. Communications of the ACM, 47,pp. 34–40.

TANNER, C., MIGDAL, C. and JONES, M., 1998, The Clipmap: a virtual mipmap. InSIGGRAPH’ 98, 26 September 1998, New York, NY.

TEMPLE LANG, D., 2010, RKML. Available online at: http://www.omegahat.org/RKML/TOOTH, S., 2006, Virtual globes: a catalyst for the re-enchantment of geomorphology. Earth

Surface Processes and Landforms, 31, pp. 1192–1194.


TURK, F.J., HAWKINS, J., RICHARDSON, K. and SURRATT, M., 2011, A tropical cycloneapplication for virtual globes. Computers & Geosciences, 37, pp. 13–24.

WEBLEY, P.W., 2011, Virtual globe visualization of ash-aviation encounters, with the specialcase of the 1989 Redoubt-KLM incident. Computers & Geosciences, 37, pp. 25–37.

WEBLEY, P.W., DEAN, K., BAILEY, J.E., DEHN, J. and PETERSON, R., 2009, Automatedforecasting of volcanic ash dispersion utilizing virtual globes. Nature Hazards, 51,pp. 345–361.

WEIDMANN, N.B. and KUSE, D., 2009, WarViews: visualizing and animating geographic dataon Civil War. International Studies Perspectives, 10, pp. 36–48.

WHITE, D. and KIESTER, A.R., 2008, Topology matters: network topology affects outcomesfrom community ecology neutral models. Computers, Environment and Urban Systems,32, pp. 165–171.

WHITE, D., KIMMERLING, J. and OVERTON, W.S., 1992, Cartographic and geometric com-ponents of a global sampling design for environment monitoring. Cartography &Geographical Information Systems, 19, pp. 5–22.

WIKIPEDIA, 2010, Available online at: http://en.wikipedia.org/wiki/Virtual_globeWOOD, J., DYKES, J., SLINGSBY, A. and CLARKE, K., 2007, Interactive visual exploration

of a large spatio-temporal dataset: reflections on a geovisualization mashup. IEEETransactions on Visualization and Computer Graphics, 13, pp. 1176–1183.

WXANALYST, 2010, W XAZYGY User Guide v21r. Available online at: http://wxanalyst.com/WxAzygy/WxAzygy_Users_Guide_v21r.pdf

XU, B., GONG, P., SETO, E., LIANG, S., YANG, C., WEN, S., QIU, D., GU, X.G. and SPEAR, R.,2006, A spatial-temporal model for assessing the effects of inter-village connectivity inschistosomiasis transmission. Annals of AAG, 96, pp. 31–46.

YAMAGISHI, Y., SUZUKI, K., TAMURA, H., NAGAO, H., YANAKA, H. and TSUBOI, S., 2006,Integration of geophysical and geochemical data. EOS Transaction of AmericanGeophysical Union, 87, Abstract IN11A–1142.

YAMAGISHI, Y., YANAKA, H., SUZUKI, K., TSUBOI, S., ISSE, T., OBAYASHI, M., TAMURA, H.and NAGAO, H., 2010, Visualization of geoscience data on Google Earth: developmentof a data converter system for seismic tomographic models. Computers & Geosciences,36, pp. 373–382.

YUAN, X., LIU, Y., YUEN, D., CHEN, B., PERGLER, T. and SHI, Y., 2008, An efficient system forcreating synthetic InSAR images from simulation. Pure and Applied Geophysics, 165,pp. 671–691.

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