Mingke Erin Li

Roles of Digital Earth

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The concept of Digital Earth plays multiple roles across research, education, and public engagement. These roles have been broadly categorized into five types: mapping and visualization tool, analytical and modeling tool, underlying basis for derivative applications, data storage structure, and platform for Volunteered Geographic Information (VGI) [1].

Mapping and Visualization Tool

Environmental Monitoring

One of the most prominent uses of Digital Earth is dynamic, real-time, and multi-scale visualization of environmental phenomena. It has been adopted by government organizations and scientific institutions to enhance understanding of environmental challenges, such as monitoring sea ice in the Arctic [2], oil spill imaging [3], disease spread [4], and species habitat distribution [5].

Interactive Visualization

Digital Earth enables users to explore spatial data interactively, enhancing public participation and environmental awareness. Examples of such applications include Common Ground [6], the Arctic Research Mapping Application [7], and Crusta [8].

Decision-Making Support

By layering contextual information over satellite imagery or base maps, Digital Earth supports decision-making in areas like disaster response [9], epidemic management [10], energy infrastructure [11], and global meta-analyses [12].

Dissemination of Research Results

Scientists can use Digital Earth as a platform to communicate findings and educate the public. Yuan et al. [13] simulated fault displacement from a tsunamigenic earthquake and visualized the results in Google Earth. Other studies visualized tsunami hazard zones in the Indian Ocean [14] and interpolated grass lineage richness on a global hexagonal grid [15].

Analytical and Modeling Tool

Basic Areal Unit

In ecological fields such as ornithology, agriculture, and wildlife biology, DGGS cells—especially hexagons—serve as equal-area, seamless analytical units. These have been used in bird migration studies to estimate species assemblages [16], summarize species richness [17], map presence or absence globally [18], analyze climate-migration associations [19], explore migration destinations [18], and predict future transatlantic routes under climate change [20]. In some cases, DGGS cells have also functioned as transitional analytical layers within study pipelines [21].

Data Integration

DGGS greatly facilitates integration of data from various sources by aligning everything within a uniform spatial framework. For example, hexagonal grids were used to combine distribution data for over 1000 chondrichthyan species across global marine environments [22].

Model Basis

Various geophysical models have been constructed on DGGS grids. These include global shallow-water models on twisted icosahedrons [23, 24] and hexahedrons [25–27], atmospheric models on spherical grids [28, 29], and a vector tile model on pole-oriented quadrangle meshes [30, 31]. Peterson further noted that DGGS is compatible with modeling techniques such as finite element, agent-based, and cellular automata due to its structured cell architecture [32].

Sampling Design Approach

The equal-area nature of DGGS allows for statistically sound sampling across global domains. Gong et al. [34] partitioned Earth using hexagons and then randomly assigned samples within each cell to test land cover map accuracy. This sampling design was reused in multiple validation studies [35, 36].

Common Reference Frame

DGGS provides a neutral, consistent frame of reference to compare results across datasets and studies. For instance, urban extent validation has been conducted using DGGS instead of administrative boundaries [37, 38]. Soil moisture datasets were also compared using ISEA 4H9 grids to enable cross-validation for remote sensing products [39, 40], including those from the Soil Moisture and Ocean Salinity (SMOS) Mission [41].

Underlying Basis for Other Derivative Applications

HEALPix, a widely used DGGS, has served as the basis for numerous cosmological software applications. These include simulations of polarized cosmic microwave background radiation, sky mapping in infrared and submillimeter wavelengths, and calculations of photon flux from dark matter annihilation or decay [42].

Data Storage Structure

SMOS products are delivered using ISEA 4H9 grids, which have been identified as the most suitable option among several global grid systems including Latitude–Longitude, UTM, QTM, and EASE grids [41]. These gridded data have supported research in biology, ecology, and oceanography [43–45].

Platform for Volunteered Geographic Information

VGI, introduced by Goodchild [1], describes citizen-contributed geospatial data. It emphasizes active human involvement and complements professional datasets by providing real-time, low-cost observations. De Longueville et al. [47] proposed integrating VGI with Sensor Web Enablement, allowing citizens to act as virtual sensors. In a forest fire scenario, this model enabled timely data reporting for crisis response. Another example demonstrated how Digital Earth supports participatory urban planning using Web Services and Service-Oriented Architecture, allowing users to compare development plans within a 3D globe environment [48].

Citation

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Digital Earth continues to serve as a versatile, scalable, and integrative platform that supports everything from scientific research and education to public participation and global policy-making.